Kittler and Spreadsheet Logic
Posted on | July 19, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, Jul 19) Kittler and Spreadsheet Logic. apennings.com https://apennings.com/spreadsheet-logic/kittler-and-spreadsheet-logic/
Introduction
Friedrich Kittler famously argued that “media determine our situation” (Kittler, 1999). Against traditions that privileged human agency, meaning, or communication, Kittler insisted that the material and technical operations of media shape the conditions under which humans think, communicate, and organize society. Technologies are not passive instruments serving human intentions; rather, humans become participants within technological systems whose logic increasingly exceeds conscious control. If Kittler were writing today, he would likely see the digital spreadsheet, not merely the computer, as one of the decisive media through which this transition occurred.
The digital spreadsheet represents a crucial historical moment in the displacement of human cognition by computational processes. Earlier media, such as writing, ledgers, and printed tables, required humans to perform the interpretive and computational work. The spreadsheet remediated these textual technologies into a programmable environment where formulas automatically executed operations that previously depended upon clerks, accountants, and managers. What appeared to be a simple office application was, in fact, a profound transformation of media. Spreadsheet logic shifted computation from human mental activity into an autonomous computational substrate.[1]
Viewed through Kittler’s perspective, spreadsheets did far more than accelerate accounting. They established a new computational grammar built upon the organization of alphanumeric inscriptions into cells, rows, columns, tables, formulas, and functions. These elements formed an operational environment where representation and computation became inseparable. Once organizational reality had been translated into spreadsheet form, decisions increasingly followed the logic embedded in formulas rather than human deliberation. Managers no longer simply consulted spreadsheets; they managed according to spreadsheet outputs.
This transition prepared the conditions for what I have called spreadsheet logic. At its foundation lies the substitution of organizational reality with computational inscriptions. People become personnel records. Financial assets become numerical variables. Inventories become database entries. Entire organizations become collections of interconnected tables. Abstraction then organizes these inscriptions into categories and relationships that make heterogeneous phenomena comparable. Symbolic computation embeds explicit mathematical relationships among these categories through formulas and functions. Finally, telecommunications synchronization distributes these computational structures across organizational networks operating continuously around the globe.
Kittler would likely recognize this progression as evidence that computational media increasingly organize themselves according to their own operational requirements rather than human intentions. Reuters synchronized currency quotations. Bloomberg synchronized securities markets. Enterprise resource planning systems synchronized global manufacturing. Cloud platforms synchronized distributed organizations. At each stage, spreadsheet logic expanded beyond individual desktops into increasingly autonomous infrastructures that coordinated organizational activity with diminishing human intervention.
Artificial intelligence extends this trajectory rather than breaking from it. Much contemporary discussion frames AI as a machine that imitates human intelligence. Kittler would reject this anthropocentric interpretation. From his perspective, AI might represent another stage in the historical development of computational media. Large language models ingest the textual products of previous computational system, including documents, databases, code repositories, websites, and countless spreadsheets, not to understand them in a human sense but to construct increasingly effective mechanisms for generating and transforming computational outputs. AI is therefore less a simulation of the human mind than the latest expression of computational media recursively operating upon their own products.
This observation becomes even more striking with autonomous AI agents. Traditional software required a human operator to initiate each computational process. The familiar sequence was straightforward:
Human => Software => Output
Large language models initially appeared to preserve this relationship through prompt engineering:
Human Prompter => AI Model => Response
Autonomous agentic systems fundamentally alter this architecture.
Human prompts increasingly become optional rather than essential:
Human (optional) => AI Agent Network => Automated API Execution => Machine-to-Machine Coordination
Once AI agents invoke APIs, retrieve data, write software, evaluate results, and trigger additional computational processes, the human increasingly disappears from the operational loop. Computational media begin interacting directly with other computational media. The system becomes recursively self-referential.
This development suggests an extension of Kittler’s thesis. Whereas Kittler argued that media determine our situation, autonomous AI demonstrates that media increasingly determine one another. Computational systems now generate, evaluate, and execute the outputs of other computational systems without requiring continuous human interpretation. Spreadsheet logic, databases, cloud platforms, blockchain networks, and AI agents become components within a larger computational ecology whose primary interactions occur machine-to-machine.
Here, the concept of operative mediation extends Kittler in an important way. Kittler emphasized the autonomy of media technologies, but operative mediation focuses on the transition from representation to coordinated action. Spreadsheet logic initially represented organizational activity. As computational infrastructures expanded through telecommunications networks, these representations became mechanisms for organizing supply chains, financial markets, logistics, and institutional workflows. AI further transforms these infrastructures by evaluating alternatives, optimizing resource allocation, and automatically initiating actions. Computation no longer merely represents organizations; it increasingly performs them.
This progression also repositions the spreadsheet within media history. It was not simply a successful business application but the medium that transformed textual representation into computational organization. Writing recorded knowledge. Accounting standardized it. Spreadsheet logic rendered it computational. Networks synchronized it. Artificial intelligence evaluates and increasingly coordinates it. The spreadsheet therefore occupies the pivotal historical position between textual media and autonomous computational infrastructures.
Kittler might ultimately argue that the arrival of autonomous AI agents confirms his most provocative claim. Humans were never the permanent center of media systems but transitional participants in a much longer history of computational development. Yet spreadsheet logic reveals that this transition was neither sudden nor inevitable. It emerged through decades of increasingly sophisticated computational grammars that progressively relocated organizational intelligence from human cognition into interconnected technical infrastructures.[2]
The result is what might be called the autonomy of the silicon sovereign. Decisions increasingly emerge from computational systems operating upon computational representations generated by other computational systems. Human participation remains politically, ethically, and legally significant, but operationally it is becoming less central to the execution of complex organizational processes. The spreadsheet, once viewed as an electronic ledger, now appears as the historical medium through which computation first became an organizational engine. AI is not its replacement but its continuation, extending spreadsheet logic into an era of operative mediation in which the primary content of computational media is increasingly other computational media.
References
Kittler, F. A. (1999). Gramophone, Film, Typewriter. Stanford University Press.
Kittler, F. A. (1997). Literature, Media, Information Systems. Routledge.
Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding New Media. MIT Press.
McLuhan, M. (1964). Understanding Media: The Extensions of Man. McGraw-Hill.
McKenzie, D. (2008) An Engine, Not a Camera. How Financial Models Shape Markets. The MIT Press.
McKenzie https://uberty.org/wp-content/uploads/2015/02/MacKenzie-An-Engine-Not-a-Camera.pdf
Pennings, A. J. (forthcoming). The Medium and the Engine: Spreadsheet Logic, SACT, and Operative Mediation.
Winthrop-Young, G. (2011) Kindler and the Media. Polity.
Notes
[1] I first ran across Kittler in graduate school and he was also included in the media ecology group.
[2] Largely my contribution is to address the movement of media theory from representation to operative mediation.
AI Prompt(s) Extrapolate Kittler’s ideas to digital spreadsheet logic using this notes. Friedrich Kittler would completely reject any humanistic anxieties regarding algorithmic bias or alignment, pointing out that humans were merely the temporary, biological scaffolding that machines used to learn how to process symbolic grammar (Kittler, 1999). Kittler’s radical thesis that “media determine our situation” finds its ultimate empirical proof in contemporary AI. Looking strictly at the silicon, the hardware routing, and the automated neural architecture, Kittler would declare that with the advent of autonomous AI agents that write their own code and execute their own APIs across global telecommunications networks, the human operator has been successfully bypassed. AI represents the transition to a purely operative medium—a closed, self-contained system where machine-generated code is interpreted, evaluated, and executed by other machines, reducing humanity to a legacy consumer of an autonomous technological landscape.
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
USAID, ICT4D, and the Geopolitics of Treasury-Backed Liquidity for Tier 4 and 5 Countries.
Posted on | July 17, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, Jul 17) USAID, ICT4D, and the Geopolitics of Treasury-Backed Liquidity for Tier 4 and 5 Countries. apennings.com https://apennings.com/global-e-commerce/usaid-ict4d-and-treasury-backed-stablecoins-for-tier-4-and-5-countries/
Introduction
The changes in international development assistance are inextricably bound to the technological infrastructure of its era. For decades, the structural dynamics of global aid operated through traditional institutional pipelines, attempting to balance economic growth against localized structural constraints. Yet, the foundational challenge of the peripheral global economy has remained remarkably persistent – the chronic and destabilizing shortage of USD (United States dollar plus global Eurodollar) liquidity.
Under the structural realities of the global dollar standard, international development agencies and technological frameworks must shift from passive observation to active engineering. While the United States Agency for International Development (USAID) has historically faced institutional and budgetary retrenchment, the soon to be renewed (hopefully) agency, when paired with Information and Communication Technologies for Development (ICT4D) initiatives, can pivot toward a more efficient, tokenized model of USD distribution and utilization for Tier 4 and 5 countries.
When the historical framing of international development is juxtaposed against contemporary breakthroughs in digital asset innovations and law, specifically the passage of the GENIUS Act, a radical structural transition becomes visible. By tracking this stablecoin trajectory through the SACT framework (Substitution, Abstraction, Symbolic Computation, Telecommunications Synchronization), and SACT-AI monetary coordination, we can map how the international monetary architecture is shifting from the descriptive limits of “spreadsheet capitalism” to an environment of automated, operative execution, based on distributed stablecoins.
We are transitioning away from a world where economic progress is bottlenecked by the physical acquisition of paper banknotes. By embracing the spatial and computational logic of distributed digital ledgers and devices, the international community can construct an intelligent, self-synchronizing liquidity infrastructure, transforming the grammar of development assistance and giving Tier 4 and 5 economies the direct tools required to participate equitably in global commerce.
The Rise and Retrenchment of USAID
The United States Agency for International Development (USAID) was established in 1961 under the Foreign Assistance Act, crystallizing a post-war civilizational milestone in public management. Its explicit goals were anchored in modernization theory. This held that stimulating long-term economic growth and building institutional capacity could foster economic growth and social development across post-colonial nations. For decades, USAID functioned as a core instrument of soft-power statecraft, deploying technical expertise, agricultural infrastructure, and public health resources.
However, this institutional framework faced severe structural retrenchment during the first Trump administration. Driven by an “America First” foreign policy paradigm, the administration targeted USAID with substantial proposed budget cuts, administrative consolidations, and an explicit pivot away from traditional multilateral development grants toward transactional, bilateral partnerships.[1]
This era marked the visible decline of post-war bureaucratic aid models, exposing a critical structural vulnerability. When domestic political priorities shift at the hegemonic core, the flow of development capital to the global periphery can be instantly restricted with severe consequences.
Defining the Global Liquidity Tiers
The decline of traditional aid models left the developing world exposed to the underlying structural inequalities of the global dollar standard. To analyze this vulnerability, modern economic management grids divide the international monetary system into explicit macro-liquidity tiers.
Tier 1 – The US is at the center as the primary US dollar issuer, and more important, the regulator of USD globally.
Tier 2 – USD Co-issuers such as Caymans, London, Frankfurt, Singapore, and UAE) lend out Eurodollars, while Tier 3 countries (China, Taiwan, Vietnam, Mexico) gather surplus USD through exports.
Tier 4 – Emerging Markets (Conditional Integration Countries)
Economies such as India, Brazil, and major Southeast Asian nations like Indonesia possess domestic industrial capacity but experience episodic, volatile access to USD liquidity. When the Federal Reserve tightens monetary policy, capital undergoes rapid flight from Tier 4 markets back to the core, triggering sharp currency devaluations and sudden balance-of-payments shocks.
Tier 5 – Frontier and Peripheral Economies
Comprising most of sub-Saharan Africa, Central Asia, and isolated Pacific island nations, Tier 5 economies exist in a permanent state of structural exclusion and constraint. Western commercial banking networks have systematically enacted “de-risking” protocols, pulling out of correspondent banking relationships due to stringent compliance costs. Consequently, Tier 5 nations are chronically starved of the physical USD liquidity required to invoice basic imports, purchase fuel, or access international trade networks.
The GENIUS Act – Absorb the Deficit, Export the Ledger
Into this structural vacuum stepped a profound legislative rewrite of the American digital asset perimeter. Signed into law in July 2025, the Guiding and Establishing National Innovation for US Stablecoins (GENIUS) Act created the first comprehensive federal regulatory framework for payment stablecoins. The act mandates that approved, non-bank issuers must back their digital tokens on a strict, unyielding 1:1 basis with cash and short-term US Treasury securities.
While publicly framed as a consumer-protection milestone, the macro-political economy of the GENIUS Act functions as a powerful mechanism for US sovereign debt management. By legally legitimizing dollar-pegged stablecoins as safe cash equivalents, the US government can effectively engineer a global, decentralized distribution engine for its own national deficit.
Every tokenized digital dollar circulating in a smartphone wallet anywhere on earth requires the issuer to purchase and hold a corresponding US Treasury dollar in collateral. The GENIUS Act transforms the global stablecoin market into a massive, structurally insatiable buyer of US sovereign debt, funding the US domestic deficit by embedding tokenized Treasury tokens directly into the global population’s everyday transaction habits.
[U.S. National Deficit] Issued as Treasury Securities Purchased by Stablecoin Issuers
[Global Periphery] Distributed to Digital Wallets Backed 1:1 via GENIUS Act over Blockchain networks
Bypassing the Chokepoints of Tier 4 and 5 Countries
For Tier 4 and Tier 5 countries, this regulatory integration offers a structural escape velocity from financial exclusion. By utilizing the SACT framework, we can map how the distribution of treasury-backed stablecoins bypasses legacy correspondent banking networks to deliver automated liquidity:
Substitution (S) The qualitative realities of local value, trade invoicing, and aid allocations are tokenized at the source as manifests of produced value. Instead of waiting for physical fiat clearances or navigating blocked regional interbank corridors, local actors substitute volatile local currencies with GENIUS-compliant tokens that carry a direct legal claim to US Treasury collateral.
Abstraction These tokens are organized into standardized digital wallet matrices. The tokenized wealth is stripped of localized administrative friction and structured into a clean, interoperable alphanumeric grammar(or various localized unicode scripts) common to both a street merchant in sub-Saharan Africa and an institutional clearing house in New York.
Symbolic Computation Financial interactions, distribution schedules, and micro-tariffs are managed through explicit, human-authored smart contracts. The computational logic operates deterministically. If a Tier 5 agricultural cooperative verifies its crop yield on a distributed ledger, the smart contract automatically recomputes dependencies and releases liquidity in dollars, thereby eliminating discretionary bureaucratic delays.
Telecommunications Synchronization Harnessing global satellite Internet arrays and cellular data networks, distributed blockchains serve as a version-control system for international wealth. The state of global liquidity is synchronized in real-time across borders. Disparate, geographically absent actors maintain an identical, unalterable audit trail of capital positions without requiring a localized brick-and-mortar banking infrastructure.
Conclusion
The convergence of the GENIUS Act and the subsequent legislative scaffolding of the Digital Asset Market CLARITY Act signifies a permanent transition in the grammar of international political economy. The historical era of development assistance, which relied on the centralized, vulnerable allocations of agencies like USAID, is yielding to a regime of operative mediation based on spreadsheet logic and US Treasury-backed cryptocurrencies?
In this new environment, the spreadsheet is no longer a passive administrative grid sheet used to audit past colonial expenditures. When cross-linked with blockchain synchronization and automated smart contract protocols, the gridded matrix becomes the active engine of financial execution. By exporting treasury-backed stablecoins directly to the digital wallets of Tier 4 and Tier 5 populations, the United States core secures a planetary sink for its sovereign debt while simultaneously providing peripheral economies with the liquid, non-volatile accounting units necessary to execute borderless trade.
The central arena of modern geopolitical power is no longer determined by the physical movement of foreign aid pallets, but by the code, syntax, and design of the computational grammar coordinating global economic life. The introduction of digital wallets provides new liquidity options, not just for the “unbanked” but for citizens of countries that have tradtionally been hampered in their ability to procure USD, or any stable currency for that matter.
References
Brookings Institution. (2025). Next steps for GENIUS payment stablecoins. Brookings Economic Studies.
Manovich, L. (2013). Software takes command. Bloomsbury Academic.
Mueller, M., Mathiason, J., & Klein, H. (2007). The Internet and global governance: Principles and norms for a new regime. Global Governance, 13(2), 237–254.
Paul Hastings LLP. (2025). The GENIUS Act: A Comprehensive Guide to US Stablecoin Regulation. https://www.paulhastings.com/insights/crypto-policy-tracker/the-genius-act-a-comprehensive-guide-to-us-stablecoin-regulation
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.
United States Senate Committee on Banking, Housing, and Urban Affairs. (2025). Myth vs. Fact: The GENIUS Act.
Notes
[1] See Handel, D. Reforming Foreign Assistance. In National Affairs, No. 68, Summer 2026.
[2]
AI Prompt(s) Draft a blog post called “USAID, ICT4D, and Treasury-Backed Stablecoins for Tier 4 and 5 Countries” discussing the possbilities for USD liquidity for LDCs that have problems procuring US dolllars.
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
Tags: Stablecoins > US Treasuries > USD liquidity
Harnessing the Four Forces for Satellite Remote Sensing and Disaster Risk Reduction
Posted on | July 14, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, Jul 14) Harnessing the Four Forces for Satellite Remote Sensing and Disaster Risk Reduction. apennings.com https://apennings.com/space-systems/harnessing-the-four-forces-for-satellite-remote-sensing-and-disaster-risk-reduction/
Introduction
Physicist Michio Kaku often frames modern physics around the four fundamental forces of nature: electromagnetism, gravity, the strong nuclear force, and the weak nuclear force. Together, these forces govern everything from the binding of atomic nuclei to the motion of galaxies. Yet when it comes to satellite remote sensing and disaster risk reduction, only two of these forces play significant roles. Electromagnetism provides the sensing capability, while gravity provides the orbital platform. The strong and weak nuclear forces, although essential to the structure of matter, operate at subatomic scales and have little direct influence on Earth observation technologies.
Electromagnetism is the foundation of virtually every modern remote sensing system.[1] Satellite sensors detect electromagnetic radiation reflected, emitted, or scattered by the Earth’s surface and atmosphere. Whether observing forests, oceans, cities, or clouds, remote sensing depends on the interaction between electromagnetic waves and matter. Maxwell’s equations describe how these waves propagate through space, while radiative transfer theory explains how they are absorbed, reflected, and scattered by vegetation, soil, water, ice, and atmospheric gases. Without the electromagnetic force, there would be no satellite imagery, radar, thermal sensing, or global environmental monitoring (Lillesand et al., 2024).
Most Earth-observing satellites employ passive sensors that record naturally occurring electromagnetic radiation. Optical instruments aboard satellites such as Landsat, Sentinel-2, and MODISmeasure sunlight reflected from the Earth’s surface in visible and near-infrared wavelengths. These observations reveal vegetation health, urban expansion, water quality, and land-use change. Thermal infrared sensors measure heat emitted by the Earth’s surface, enabling scientists to detect wildfires, monitor drought conditions, estimate sea surface temperatures, and identify volcanic activity.
Other systems use active remote sensing, generating their own electromagnetic signals and measuring the returning echoes. Synthetic Aperture Radar (SAR) transmits microwave pulses that can penetrate clouds, smoke, and darkness, enabling continuous observations regardless of weather or time of day. By comparing the phase of successive radar images through Interferometric Synthetic Aperture Radar (InSAR), scientists can detect ground deformation of only a few millimeters associated with earthquakes, volcanic inflation, landslides, and subsidence. Similarly, lidar systems emit laser pulses to create highly accurate three-dimensional maps of forests, coastlines, and urban infrastructure.
Although gravity is the weakest of the four fundamental forces, it plays an indispensable supporting role. Gravity keeps satellites in orbit, enabling the repeated observations necessary for long-term environmental monitoring. More importantly, subtle variations in Earth’s gravitational field provide valuable information about changes in the planet’s mass distribution. Missions such as NASA and the German Aerospace Center’s GRACE and GRACE Follow-On (GRACE-FO) measure tiny changes in the distance between paired satellites as they pass over regions with slightly different gravitational attraction. These variations reveal shifts in groundwater storage, melting glaciers, ice sheets, and large-scale hydrological processes that cannot be observed directly using optical imagery alone (Tapley et al., 2019).
Gravity-based remote sensing therefore complements electromagnetic observations rather than replacing them. While radar and optical sensors map the visible consequences of floods or droughts, gravimetric measurements reveal changes in underground water storage months before those changes become apparent at the surface. During the severe Missouri River flooding of 2011, GRACE measurements detected unusually high terrestrial water storage well before catastrophic flooding occurred, demonstrating how gravity observations can strengthen hydrological forecasting and disaster preparedness.
These complementary sensing technologies now support every phase of Disaster Risk Reduction (DRR). Before disasters occur, satellite observations identify vulnerable populations, unstable slopes, active fault zones, groundwater depletion, and drought conditions, supporting risk assessment and mitigation planning. During unfolding disasters, satellites provide near real-time monitoring of hurricanes, floods, wildfires, volcanic eruptions, earthquakes, and tsunamis. High-resolution optical imagery documents damage to infrastructure, while SAR penetrates cloud cover to map flooded regions even during severe storms. Thermal infrared sensors identify wildfire hotspots and monitor lava flows, while microwave observations estimate rainfall intensity, soil moisture, and snowpack conditions that influence flooding.
Following disasters, remote sensing plays an equally important role in recovery. Damage assessments guide emergency response teams, insurers, and governments by rapidly identifying destroyed buildings, damaged transportation networks, and displaced populations. Continuous monitoring tracks reconstruction, infrastructure stability, and environmental recovery over months or years. International collaborations such as the International Charter “Space and Major Disasters” and the Copernicus Emergency Management Service coordinate satellite resources worldwide to provide timely information during humanitarian emergencies.
The strong and weak nuclear forces, despite their central importance in physics, contribute little to these remote sensing capabilities. The strong nuclear force binds protons and neutrons within atomic nuclei, while the weak nuclear force governs radioactive decay and nuclear reactions. Although these forces underlie the atomic structure of materials observed by satellites and contribute indirectly to the operation of specialized radiation detectors, they do not influence the propagation or detection of the electromagnetic signals that constitute the overwhelming majority of Earth observation systems.
Looking ahead, next-generation satellite missions promise to strengthen disaster monitoring further. The NASA–ISRO Synthetic Aperture Radar (NISAR) mission combines L-band and S-band radar to improve deformation monitoring, biomass estimation, and vegetation mapping. Large constellations of commercial satellites now provide revisit times measured in hours rather than days, while advances in cloud computing and artificial intelligence enable continuous analysis of enormous volumes of satellite imagery. These technologies are transforming remote sensing from a system of periodic observation into one of continuous environmental monitoring.
Viewed through Michio Kaku’s framework, satellite remote sensing demonstrates humanity’s remarkable ability to harness the forces of nature. Electromagnetism enables satellites to observe the Earth’s surface with extraordinary precision, while gravity sustains the orbital platforms that enable continuous monitoring. Together, these two forces have become indispensable tools for protecting lives, managing natural resources, and strengthening resilience against an increasingly dynamic and hazardous planet.
References
Kaku, M. (1994). Hyperspace: A Scientific Odyssey Through Parallel Universes, Time Warps, and the Tenth Dimension. Oxford University Press.
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2024). Remote Sensing and Image Interpretation (8th ed.). Wiley.
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., … & Velicogna, I. (2019). Contributions of GRACE to understanding climate change. Nature Climate Change, 9(5), 358–369.
UN Office for Outer Space Affairs (UNOOSA). (2021). Space Supporting the Sendai Framework for Disaster Risk Reduction. United Nations.
United Nations Platform for Space-based Information for Disaster Management and Emergency Response (UN-SPIDER). (2023). Space-based Information for Disaster Risk Reduction and Emergency Response. United Nations.
Notes
[1] In my EST 561 – Sensing Technologies for Disaster Risk Reduction, we have gone from remote satellites to other technologies including robots, just as Boston Dynamics’ SPOT.
[2] We follow an (STS) Science, Technology, Society program popular in many engineering schools.
AI Prompt(s) Of the 4 forces of nature (EM, Gravity, Strong and Weak Nuclear) what are most important for remote sensing technologies on satellites? Describe how they are used in disaster risk reduction.
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
Tags: Four forces > Michio Kaku > remote sensing
Seeing the Earth’s Surface Move Millimeter by Millimeter with EM
Posted on | July 13, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, Jul 13) Seeing the Earth’s Surface Move Millimeter by Millimeter with EM. apennings.com https://apennings.com/space-systems/seeing-the-earths-surface-move-millimeter-by-millimeter-with-em/
Introduction
Physicist Michio Kaku often describes the history of science as humanity’s gradual mastery of the four fundamental forces of nature: gravity, electromagnetism, strong nuclear force, and weak nuclear force. Gravity shapes the structure of planets and galaxies. The strong and weak nuclear forces govern the atom and make nuclear energy possible.
Electromagnetism, however, has become the force most directly harnessed by modern civilization. Electricity powers our homes, telecommunications connect the globe, computers perform trillions of calculations each second, and satellites continuously observe the planet from orbit. Nearly every major technological achievement of the last century has been an application of electromagnetism.
I use Kaku’s Physics of the Future and its emphasis on the Four Forces in my EST 202 – Introduction to Science, Technology and Society Studies class. He proposes that focusing on the four forces is a good way to anticipate future developments in technology innovations related to semiconductors, robotics, AI, nanotechnology, medicine, space travel, etc.[1]
One remarkable example is Synthetic Aperture Radar (SAR) and its more advanced derivative, Interferometric Synthetic Aperture Radar (InSAR). These technologies exploit microwave radiation, a portion of the electromagnetic spectrum, to illuminate the Earth’s surface from space and measure changes in topography and ground movement with astonishing precision.
By comparing the phase of radar waves collected during successive satellite passes, InSAR can detect surface displacements of only a few millimeters across areas spanning hundreds of square kilometers. Invisible movements associated with earthquakes, volcanic inflation, landslides, groundwater depletion, glacier flow, and urban subsidence become visible through careful analysis of electromagnetic signals.
What makes InSAR especially significant is that it transforms electromagnetic energy into a sophisticated measurement system for monitoring the dynamic Earth. Rather than relying on visible light, which is blocked by darkness and clouds, radar actively transmits its own microwave pulses and records the returning echoes under virtually all weather conditions, day or night. Advances in satellite engineering, signal processing, and computational modeling have transformed these radar measurements into one of the world’s most important tools for disaster risk reduction, infrastructure monitoring, and environmental management.
In many ways, InSAR illustrates a broader pattern in modern science and engineering. The fundamental forces of nature remain constant, but our ability to measure, compute, and interpret their effects continues to improve dramatically. By harnessing electromagnetism through increasingly sophisticated computational techniques, InSAR allows us to observe the Earth’s subtle motions with extraordinary accuracy, providing governments, scientists, and engineers with an unprecedented capability to anticipate hazards and better understand the dynamic processes continually reshaping our planet.
The Earth’s surface is constantly moving. Earthquakes shift fault lines, volcanoes inflate before eruptions, groundwater extraction causes cities to sink, and slow-moving landslides threaten communities long before disaster strikes. Detecting these subtle changes across hundreds of kilometers was once nearly impossible. Today, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most powerful remote sensing technologies for monitoring Earth’s dynamic surface, measuring changes with millimeter-to-centimeter precision from space.
InSAR builds upon Synthetic Aperture Radar (SAR), an active microwave imaging technology carried aboard satellites. Unlike optical cameras, SAR does not depend on sunlight and can image the Earth’s surface day or night, through clouds, smoke, and many weather conditions. The satellite transmits microwave pulses using C-, X-, L-, or S-band frequencies and records the reflected signals returning from the ground. Each reflected signal contains two important pieces of information. The first is its amplitude, which indicates how strongly the surface reflects the radar energy, and the other is its phase, which records the distance traveled by the microwave pulse. While a single SAR image provides valuable information about surface roughness, vegetation, moisture, and land cover, its phase measurements alone cannot reliably determine elevation or ground movement because they contain multiple ambiguities.
InSAR overcomes this limitation by comparing two or more SAR images acquired over the same location. The technique computes the difference in phase between corresponding pixels to create an interferogram, effectively canceling many common signal components while revealing minute differences in the radar path length. These differences correspond to changes in elevation or movement of the Earth’s surface along the satellite’s line of sight. Even displacements of only a few millimeters can produce measurable phase changes because radar wavelengths are only a few centimeters long (Hanssen, 2001).
The measured interferometric phase reflects several physical processes operating simultaneously. Surface deformation caused by earthquakes, volcanic inflation, subsidence, or landslides contributes directly to the observed phase shift. Topographic relief also affects the signal because higher terrain changes the radar travel distance. Additional contributions arise from the Earth’s curvature, atmospheric water vapor and ionospheric conditions, orbital geometry, and various sources of noise caused by changes in vegetation or surface conditions between satellite passes. Separating these influences requires sophisticated processing techniques and careful calibration (Massonnet & Feigl, 1998).
The basic processing workflow begins by precisely aligning multiple SAR images so that each pixel corresponds to the same ground location. An interferogram is then generated from the complex radar signals, followed by removal of the known topographic contribution using an external digital elevation model (DEM). Because radar phase repeats every wavelength, specialized phase unwrapping algorithms reconstruct the true displacement field by resolving these ambiguities. Atmospheric corrections, orbital refinements, and geocoding transform the processed interferogram into accurate maps of surface deformation.
As satellite archives have expanded, InSAR has transitioned from analyzing individual image pairs to long-term monitoring of surface change. Differential InSAR (DInSAR) measures deformation between a small number of acquisitions, while advanced time-series approaches dramatically improve measurement accuracy. Persistent Scatterer InSAR (PS-InSAR) identifies highly stable reflectors such as buildings, bridges, or exposed rock that remain coherent over many years, allowing continuous monitoring of urban infrastructure and subsidence. Small Baseline Subset (SBAS) techniques instead combine numerous closely spaced image pairs, making it possible to monitor deformation even in areas with moderate vegetation or changing surface conditions (Ferretti et al., 2001; Berardino et al., 2002).
One of the key concepts underlying InSAR is coherence, a measure of how consistently radar signals are reflected between acquisitions. High coherence indicates that the same surface features continue to reflect radar energy in nearly identical ways, enabling highly accurate deformation measurements. Vegetation growth, snowfall, flooding, agricultural activity, or rapid landscape change reduce coherence and make reliable measurements more difficult. Longer radar wavelengths, such as L-band, often maintain coherence better in vegetated environments because they penetrate foliage more effectively than shorter wavelengths.
The ability to monitor ground deformation across vast regions has made InSAR an indispensable tool for disaster risk reduction. Following major earthquakes, InSAR rapidly maps fault displacement and ground deformation, providing critical information for estimating fault geometry and assessing damage. Since its landmark application to the 1992 Landers earthquake in California, InSAR has become a standard technique in earthquake science and tectonic monitoring. Volcano observatories routinely use InSAR to detect subtle inflation or deflation of magma chambers that may precede eruptions. Slow-moving landslides can be monitored over months or years, providing early warning before catastrophic slope failure. Urban planners use InSAR to track subsidence caused by groundwater extraction, mining, or thawing permafrost, while engineers monitor dams, bridges, pipelines, and transportation infrastructure for early signs of structural instability (Massonnet & Feigl, 1998; Crosetto et al., 2016).
The advantages of InSAR are considerable. A single satellite acquisition can measure surface deformation over hundreds of square kilometers with meter-scale spatial resolution while maintaining millimeter-to-centimeter precision. Because radar actively illuminates the Earth’s surface, observations continue regardless of daylight or cloud cover, making InSAR uniquely suited for continuous global monitoring.
Despite these strengths, important limitations remain. InSAR measures only displacement along the radar’s line of sight, requiring observations from multiple viewing geometries or integration with Global Navigation Satellite Systems (GNSS) to reconstruct full three-dimensional ground motion. Atmospheric water vapor remains a major source of measurement error, although modern time-series methods and atmospheric correction models have greatly reduced these effects. Dense vegetation, snow cover, rapid landscape change, and large deformation gradients can also reduce coherence and complicate phase unwrapping.
The rapid expansion of satellite missions has transformed InSAR from a research tool into an operational monitoring system. The European Space Agency’s Sentinel-1 constellation provides freely available C-band imagery with revisit intervals of six to twelve days, making routine deformation monitoring accessible worldwide. The recently launched NASA–ISRO Synthetic Aperture Radar (NISAR) mission adds complementary L- and S-band observations that improve monitoring of vegetation, biomass, earthquakes, glaciers, and ground deformation in heavily vegetated regions. Additional missions such as TerraSAR-X, COSMO-SkyMed, RADARSAT, and Gaofen-3 continue to expand global radar coverage.
Increasingly, InSAR is combined with GNSS measurements, optical satellite imagery, weather models, and numerical simulations to create comprehensive hazard-monitoring systems. Rather than providing isolated snapshots of the Earth’s surface, modern InSAR supports continuous time-series analysis capable of detecting gradual environmental changes before they become disasters. As satellite constellations expand and processing techniques continue to improve, InSAR is transitioning into one of the foundational technologies for monitoring a changing planet and enhancing global resilience to natural hazards.
References
Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11), 2375–2383.
Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., & Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78–89.
Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20.
Hanssen, R. F. (2001). Radar Interferometry: Data Interpretation and Error Analysis. Springer.
Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth’s surface. Reviews of Geophysics, 36(4), 441–500.
Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3), 333–382.
Notes
[1] A good introduction to Kaku and the four forces is the video – The Universe in the Nutshell.
AI Prompt(s) Explore InSAR technology details
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
The Historical Transitions from Human Expertise to AI Techno-Epistemologies
Posted on | July 8, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, Jul 08) The Historical Transitions from Human Expertise to AI Techno-Epistemologies. apennings.com https://apennings.com/social_media/the-historical-transitions-from-human-expertise-to-ai-epistemologies/
Introduction
Viewed historically, each information and communication technology (ICT) extends rather than replaces the previous one. Viewed historically, ICT have not displaced one another so much as remediated and extended the symbolic and computational capacities of earlier media. Each new medium preserves the major functions of its predecessors while introducing a new dominant logic that expands the scale, speed, and sophistication of organizational coordination.[1]
The sequence begins with writing, whose primary contribution was the ability to record knowledge across time and space. Writing transformed speech into durable symbols, allowing organizations, governments, and civilizations to preserve memory beyond the limits of individual cognition. Laws, contracts, inventories, and correspondence became stable records that could coordinate human activity across generations.[2]
Building upon writing, accounting introduced a new logic of classification. Rather than simply recording events, accounting organized them into standardized categories such as assets, liabilities, revenues, expenses, and inventories. This classificatory grammar made economic activity comparable and governable. Merchants, states, and later corporations could systematically monitor resources, evaluate performance, and coordinate increasingly complex organizations through common symbolic categories.[3]
The digital spreadsheet transformed accounting once again by introducing explicit symbolic computation. Rows, columns, cells, formulas, and functions converted static accounting records into dynamic computational models. Relationships that had once existed only in the analyst’s reasoning became encoded directly into formulas that could instantly recalculate entire financial systems. The spreadsheet therefore shifted organizations from simply recording and classifying reality to actively modeling alternative futures through “what-if” analysis, forecasting, budgeting, and financial simulation.[4]
The emergence of the database extended spreadsheet logic into persistent organizational memory. Databases emphasized storage and retrieval, allowing enormous quantities of structured information to be maintained, queried, and shared across distributed organizations. While spreadsheets remained powerful analytical tools, databases became the authoritative repositories supporting enterprise systems, customer records, logistics, and financial transactions. They separated long-term information management from analytical computation while preserving the spreadsheet’s underlying symbolic grammar.
Machine learning introduced another important transition by replacing explicit formulas with statistical inference. Instead of requiring analysts to specify mathematical relationships manually, machine-learning algorithms estimate those relationships directly from data. Human-authored formulas give way to learned parameters that continually adjust through optimization. The focus shifts from representing known relationships to discovering previously unknown patterns, enabling prediction, classification, anomaly detection, and adaptive decision support.
Large language models (LLMs) extend machine learning beyond numerical prediction into semantic generation and reasoning. Trained on vast corpora of text, code, images, and other symbolic media, they recognize relationships among concepts, synthesize knowledge, generate coherent language, and assist human reasoning across diverse domains.
They remediate not only spreadsheets and databases but also books, libraries, encyclopedias, search engines, programming languages, and organizational documents into a unified semantic environment capable of interacting through natural language.
The next transition is what I describe as SACT-AI. Rather than focusing solely on representation, storage, prediction, or language generation, SACT-AI introduces the dominant logic of coordination. Drawing upon the symbolic grammar established by writing, accounting, spreadsheets, databases, and machine learning, SACT-AI integrates these capabilities into a globally distributed infrastructure that continuously monitors, evaluates, and coordinates economic activity. Within this architecture, symbolic representations become operational. Artificial intelligence no longer simply analyzes information or predicts outcomes; it coordinates liquidity, telecommunications, logistics, governance, and organizational processes across synchronized digital networks.
This historical progression illustrates a broader transition in the role of symbolic media. Writing enabled societies to remember. Accounting enabled them to organize. Spreadsheets enabled them to compute. Databases enabled them to store and retrieve. Machine learning enabled them to infer. Large language models enabled them to reason semantically. SACT-AI extends this trajectory by enabling institutions to coordinate distributed economic activity continuously across time and space.
This is the central historical argument. The future of artificial intelligence is not a break with spreadsheet logic but its operational extension. Spreadsheet logic remains the symbolic and accounting grammar inherited from centuries of organizational practice. AI remediates that grammar through statistical learning and semantic reasoning, while SACT-AI transforms it into an infrastructure for operative mediation in which representation, inference, and coordination become integrated within a globally synchronized system. The defining question for the next generation of information systems is therefore no longer simply how organizations compute knowledge, but how they coordinate action through shared symbolic and computational infrastructures.
References
Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding New Media. MIT Press.
Giddens, A. (1984). The Constitution of Society: Outline of the Theory of Structuration. University of California Press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Grossman, Tomas A.; Mehrotra, Vijay; and Özlük, Özgür (2007) “Lessons from Mission-Critical Spreadsheets,” Communications of the Association for Information Systems: Vol. 20 , Article 60. DOI: 10.17705/1CAIS.02060
https://aisel.aisnet.org/cais/vol20/iss1/60
McCarthy, J. (2007). What is artificial intelligence? Stanford University. (Original work written 2004).
Goody, J. (1986) The Logic of Writing and the Organization of Society. Studies in Literacy, Family, Culture and the State. (Cambridge: Cambridge University Press).
Levy, S. (1989) “A Spreadsheet Way of Knowledge,” in Computers in the Human Context: Information Technology, Productivity, and People. Tom Forester (ed.) (Oxford: Basil Blackwell). He gave me permission to put the article on my website when I was at NYU.
McLuhan, M. (1964). Understanding Media: The Extensions of Man. McGraw-Hill.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–428.
Poovey, M. (1998). A History of Modern Fact: Problems of Knowledge in the Sciences of Wealth and Society. University of Chicago Press.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
Notes
[1] Remediation is a constant theme in my work on Spreadsheet Logic and AI because it follows a tradition of media interrogation that focuses on how newer media incorporate older media. What are its implications for epistemology? For the production of power? Newer technologies influence on society such as television, which integrated radio and film.
[2] For an in-depth analysis of writing I liked Giddens’ use of Goody, J. (1986) The Logic of Writing and the Organization of Society. Studies in Literacy, Family, Culture and the State. (Cambridge: Cambridge University Press).
[3] Mary Poovey’s A History of Modern Fact offers one of the most penetrating accounts of how modern economic knowledge came to appear objective, factual, and politically neutral. Poovey, M. (1993) “Figures of Arithematic, Figures of Speech: The Discourse of Statistics in the 1830’s,” Critical Inquiry. Winter, Vol. 19, No. 2.
[4] In an extraordinary chapter Steven Levy’s Levy, S. (1989) “A Spreadsheet Way of Knowledge,” in Computers in the
Human Context: Information Technology, Productivity, and People. Tom Forester (ed.) (Oxford: Basil Blackwell).
AI Prompt(s) What is difference between spreadsheet models and the machine-learning models?
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
Tags: techno-epistemological
From Spreadsheet Models to Machine Learning: The Transition from Representation to Prediction
Posted on | July 7, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, July 07) From Spreadsheet Models to Machine Learning: The Transition from Representation to Prediction. apennings.com https://apennings.com/characteristics-of-digital-media/from-spreadsheet-models-to-machine-learning-the-transition-from-representation-to-prediction/
Introduction
A common narrative is that AI “replaces” spreadsheets. I argue instead that machine learning “remediates” spreadsheet logic. It inherits the spreadsheet’s dynamic grid and symbolic grammar while transforming how relationships among variables are discovered and used. The key distinction lies in where knowledge resides and produces action.[1]
In a spreadsheet model, knowledge is explicit. Humans decide which variables matter, how they are organized, and how they relate mathematically. The analyst constructs the model cell by cell. Every formula embodies a hypothesis about how the world works. If interest expense equals debt multiplied by the interest rate, someone writes the formula. If GDP is projected to grow by 3 percent, someone specifies that assumption. Spreadsheet models therefore represent human theories encoded as symbolic formulas.
Machine-learning models operate differently. Rather than encoding relationships explicitly, they infer statistical relationships from data. Instead of writing the formula, the programmer defines an objective function and a learning algorithm. During training, the model adjusts millions, or billions, of parameters until it minimizes prediction error. The resulting relationships are distributed throughout the model’s weights rather than expressed as readable equations.
The distinction between spreadsheet models and machine-learning models is not simply one of technology but of how knowledge is constructed and operationalized. It can be summarized as follows:
Spreadsheet models begin with human expertise. The analyst determines which variables matter, organizes them into rows, columns, and tables, and explicitly specifies the mathematical relationships among them. Every formula represents a human hypothesis about how the world works, making the spreadsheet a symbolic representation of expert knowledge encoded in computational form.
Machine-learning models, by contrast, derive their knowledge primarily through statistical learning from data. Rather than relying on human-authored formulas, they analyze large datasets to automatically discover patterns and relationships. These relationships are not expressed as readable equations but are distributed across learned parameters, or weights, that are adjusted during training. Knowledge therefore emerges from optimization rather than explicit human design.
This difference is reflected in how each model is constructed. Spreadsheet models are built manually, with analysts writing formulas, linking cells, and testing assumptions one calculation at a time. Machine-learning models are created through automated training processes in which algorithms repeatedly adjust millions or even billions of parameters until prediction errors are minimized. The model is not programmed to solve a particular relationship; instead, it learns that relationship from examples.
The underlying logic also differs significantly. Spreadsheet models are primarily deductive. They begin with established assumptions or theoretical relationships and compute their implications. If the assumptions change, the formulas must be rewritten. Machine-learning models operate primarily through inductive statistical inference. They infer relationships directly from observed data and continuously optimize those relationships as additional information becomes available. Rather than testing a predefined theory, they discover probabilistic regularities within the data.
These contrasting approaches produce very different levels of transparency. Spreadsheet models are highly interpretable because every calculation can be traced to a visible formula and every assumption can be inspected or audited. Users can usually explain why a particular output was generated by following the chain of formulas through the workbook.
Machine-learning models are frequently far more opaque. Their decisions emerge from the interaction of vast numbers of learned parameters, making it difficult, even for their designers, to explain precisely why a particular prediction or recommendation was produced. This opacity has given rise to the characterization of many AI systems as “black boxes.”
Their capacity for adaptation also distinguishes the two approaches. Spreadsheet models generally require manual updates whenever assumptions, formulas, or business conditions change. Human analysts remain responsible for maintaining the model. Machine-learning systems, on the other hand, can be retrained periodically or updated continuously as new data becomes available. Their ability to adapt automatically allows them to respond to changing environments without requiring every relationship to be rewritten by human experts.
Ultimately, the two technologies pursue different objectives. Spreadsheet models are designed primarily to represent and analyze reality. They organize information, test scenarios, and support human decision-making through explicit computational reasoning. Machine-learning models, by contrast, are designed to predict, classify, generate, and optimize. Their primary goal is not simply to describe existing relationships but to infer new ones, forecast future outcomes, recognize complex patterns, and increasingly recommend or automate decisions.
From the perspective of spreadsheet logic, machine learning does not abandon the spreadsheet’s symbolic foundation; it extends it. Spreadsheet models express human knowledge through explicit symbolic computation, whereas machine-learning models transform that symbolic foundation into adaptive statistical inference. The progression is therefore not one of replacement but of remediation.
Spreadsheet logic re-established the accounting grammar through which organizations represented economic reality. Machine learning builds upon that grammar by learning relationships that humans no longer specify explicitly. In the broader SACT framework, this marks the transition from representation to prediction, while SACT-AI extends the process one step further toward coordination, where AI not only models and predicts organizational activity but continuously helps coordinate it across globally synchronized information infrastructures.
In this progression each new layer retains the symbolic structures established by its predecessors while expanding their operational capabilities.
A New Theoretical Distinction
This comparison also suggests a distinction that could become central to this project. Spreadsheet models are representational models that symbolically compute. Machine-learning models are inferential models. SACT-AI coordination systems are operational models.
– Representational models address, “What is happening?”
– Inferential models address, “What is likely to happen?”
– Operational models answer, “What should the system do next?”
That final transition is what I have been calling operative mediation. It is not simply a new computational technique but a new organizational logic. Spreadsheet logic made organizations computational; machine learning made them predictive; SACT-AI would make them continuously coordinative, integrating representation, inference, and action within a globally synchronized infrastructure.
This progression provides a theoretical bridge linking work on spreadsheet logic to contemporary AI while distinguishing it from conventional machine-learning research, which typically concentrates on predictive performance rather than the symbolic and organizational infrastructures that make prediction possible.
References
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Grossman, Tomas A.; Mehrotra, Vijay; and Özlük, Özgür (2007) “Lessons from Mission-Critical Spreadsheets,” Communications of the Association for Information Systems: Vol. 20 , Article 60. DOI: 10.17705/1CAIS.02060
https://aisel.aisnet.org/cais/vol20/iss1/60
McCarthy, J. (2007). What is artificial intelligence? Stanford University. (Original work written 2004).
McLuhan, M. (1964). Understanding Media: The Extensions of Man. McGraw-Hill.
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–428.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
Notes
[1] My focus has been on the remediation of technologies into the digital spreadsheet and its implications for techno-epistemological and the production of power.
[2] The visibility of the spreadsheet in an AI age intrigues me. From VisiCalc to Datarails, the Excel-native FP&A (Financial Planning & Analysis) platform that connects to hundreds of data sources (ERPs, CRMs, banks, HRIS), automates consolidation and reporting, adds governance and version control, and layers AI capabilities on top that transforms scattered spreadsheets into a single source of truth.
AI Prompt(s) What is difference between spreadsheet models and the machine-learning models?
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
Tags: Machine Learning
Designing a Global AI Engine for Monetary Coordination
Posted on | July 3, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, Jun 03) Designing a Global AI Engine for Monetary Coordination. apennings.com https://apennings.com/technologies-of-meaning/designing-a-global-ai-engine-for-monetary-coordination/
Introduction
The debate over the future of the international monetary system usually begins with the wrong question: Which currency will replace the US dollar? Will it be Bitcoin, central bank digital currencies (CBDCs), the Chinese yuan, or a BRICS alternative?
A more fundamental set of questions are: What comprises the current USD-centered system? What are its limitations? How does it affect different countries? What computational infrastructure can coordinate global liquidity more effectively? The answer may lie not in a new currency alone, but in a new architecture of accounting, computation, telecommunications, and artificial intelligence (AI).
Spreadsheet Logic as Global Infrastructure
Modern finance already operates through what I call spreadsheet logic. Every central bank, multinational corporation, commercial bank, and financial market relies on a common symbolic grammar inherited from accounting and transformed by digital spreadsheets. From Bloomberg, to Datarails, to Excel, and to ERP systems, the grammar goes through:
Words => Numbers => Lists => Tables => Cells => Formulas => Models
These elements allow organizations to represent economic activity in standardized forms that can be calculated, compared, and shared. Every balance sheet, Treasury auction, trade invoice, derivatives portfolio, and liquidity report is constructed within this accounting grammar before money ever moves.
Spreadsheet logic therefore functions as a type of operating system for global capitalism.
The SACT Architecture
The SACT framework explains how spreadsheet logic becomes operationalized.[1]
– Substitution converts economic activity and goods into digital representations.
– Abstraction organizes those representations into standardized accounting categories.
– Symbolic Computation models relationships among production, trade, finance, and risk.
– Telecommunications Synchronization distributes these representations across global digital networks, allowing institutions on different continents to coordinate from a common informational framework.
This architecture already underlies today’s financial system.
Artificial intelligence extends it.
AI as Coordination Intelligence
AI should not be viewed simply as another analytical tool. Its larger role is to become coordination intelligence. Traditional spreadsheets computed formulas written by human analysts. AI continuously evaluates millions of relationships simultaneously, identifying emerging imbalances, forecasting liquidity needs, and recommending adjustments as conditions change.
The progression becomes:
Representation => Computation => Evaluation => Coordination
Spreadsheet logic remains the symbolic and grammatological foundation; AI transforms it into an adaptive coordination system.
A Twenty-First Century Bancor
John Maynard Keynes proposed an International Clearing Union (ICU) and a supranational reserve asset—Bancor—at the Bretton Woods conference in 1944. His objective was to prevent persistent global economic imbalances by coordinating international liquidity through a neutral clearing system rather than relying on the currency of a single nation.
The proposal ultimately failed, in part because the computational infrastructure necessary to operate such a system simply did not exist. Telegraphy and tabulating machines of the time could not collect and process the information efficiently.
Today it does. Blockchain synchronizes trusted ledgers. Cloud computing enables globally distributed processing. Digital identity authenticates participants. Artificial intelligence continuously evaluates global economic conditions. Together these technologies provide the infrastructure that Keynes lacked.
Governance Through Distributed Coordination
A modern ICU need not function as a centralized world central bank. Drawing on Milton Mueller, John Mathiason, and Hans Klein’s work on Internet governance, a more practical model is a distributed coordination regime. The Internet succeeds not because one organization controls it, but because independent participants follow common protocols, interoperable standards, and shared governance principles.[2]
The same logic could apply to international monetary coordination. Central banks would retain monetary sovereignty. Commercial banks would continue serving customers. National governments would maintain fiscal authority.
AI would coordinate information, not replace institutions.
Blockchain would synchronize records, not dictate policy.
The system would operate through shared standards rather than centralized command.
Beyond Currency Competition
The future international monetary system is therefore unlikely to be determined simply by whether the dollar, yuan, Bitcoin, or CBDCs become dominant. The decisive competition will be between coordination infrastructures.
The most successful system will be the one that best integrates accounting, telecommunications, computation, governance, and artificial intelligence into a coherent platform for managing global liquidity.
Spreadsheet capitalism demonstrated that shared accounting grammars could coordinate organizations across continents. The next transition extends that logic beyond representation. By combining the spreadsheet logic of SACT with distributed networking and blockchain synchronization, AI-driven coordination would work as the modern implementation of Keynes’s International Clearing Union, technically feasible for the first time.
The challenge is no longer creating enough liquidity. It is building an intelligent infrastructure capable of coordinating it.
References
Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding New Media. MIT Press.
Keynes, J. M. (1943/1980). Proposals for an International Clearing Union. In The Collected Writings of John Maynard Keynes (Vol. 25). Macmillan.
Mueller, M., Mathiason, J., & Klein, H. (2007). The Internet and Global Governance: Principles and Norms for a New Regime. Global Governance, 13(2), 237–254.
Pennings, A. J. (forthcoming). Spreadsheet Logic, SACT, and Operative Mediation.
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
https://www.mdpi.com/2078-2489/13/2/46
Triffin, R. (1960). Gold and the Dollar Crisis: The Future of Convertibility. Yale University Press.
Notes
[1] I developed the SACT framework in the summer of 2025 primarily adding to a focus on spreadsheet formulas, (“formulating power”) to addressing the issue of “Writing as Substitution.” See Pennings, A.J. (2025, July 24) Stablecoins, Blockchains, and the Semiotic-Telecom-Computational Stack of Spreadsheet Capitalism. apennings.com https://apennings.com/artificial-intelligence/stablecoins-blockchains-and-the-semiotic-telecom-computational-stack-of-spreadsheet-capitalism/
[2] The governance of domain names (DNS) has intrigued me as a possible framework for the global SACT-AI implementation of Bancor/ICU, but domain names are not dollars, even if they have literally printed dollars for DNS registering companies.
AI Prompt(s) Let’s continue this discussion into the design of a globally distributed AI engine. Draw on The Internet and Global Governance: Principles and Norms for a New Regime by Milton Mueller, John Mathiason and Hans Klein Vol. 13, No. 2 (April–June 2007), pp. 237-254 for both the currency coordination but also the telecommunications synchronization so crucial for the SACT-AI implemention.
© ALL RIGHTS RESERVED
Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
Tags: Bitcoin > central bank digital currencies (CBDCs) > Chinese yuan > SACT (Substitution - Abstraction - Symbolic Computing - Telecommunications Synchronization) > SACT-AI
Literature Review on Global US Dollar Shortages and the Performativity of the USD Standard
Posted on | July 2, 2026 | No Comments
Citation APA (7th Edition)
Pennings, A.J. (2026, July 02) Literature Review on Global US Dollar Shortages and the Performativity of the USD Standard. apennings.com https://apennings.com/global-e-commerce/literature-review-on-global-usd-shortages-and-the-performativity-of-the-usd-standard/
The limit of a society’s wealth is ultimately determined by the structural limits of its accounting grammar. – ChatGPT [1]
Introduction
The argument that the global economy suffers from a structural shortage of USD (US dollars plus offshore Eurodollars), often referred to as the Global Dollar Shortage or the Eurodollar Scarcity Paradox, is a cornerstone of modern international macroeconomics and monetary sociology.
In this post, I examine some of the background academic work that has been done in this area. connecting it via links to previous work I have done. While it seems counterintuitive that a currency could be in short supply when the Federal Reserve has significantly expanded its balance sheet over the last two decades, the shortage is not about domestic cash. It is a shortage of offshore dollar-denominated liquidity and “pristine” collateral relative to the mountain of global debt denominated in USD.
This structural mismatch acts as a contractionary force on global trade, devalues emerging market currencies, and triggers systemic banking instability.
The Eurodollar Market and the Offshore Liabilities Mountain
To understand the dollar shortage, we must look outside the United States. The international financial system operates on the Eurodollar market, an unsupervised, borderless network of dollar-denominated deposits and loans held in banks outside the US. (McCauley et al., 2014).
According to the Bank for International Settlements (BIS), offshore dollar-denominated debt owed by non-bank entities outside the US has reached tens of trillions of dollars. Global corporations, sovereign governments, and supply chain networks routinely borrow in dollars because it is the undisputed global invoice currency.
By examing these operational layers below, the model shifts from a simple observation of cause-and-effect to a sophisticated diagnostic tool that explains the exact institutional plumbing of global spreadsheet capitalism.
[Phase 1: Structural Foundation]
Global Trade & Borrowing => Massive Accumulation of Offshore USD Liabilities (The Debt Stock)
[Phase 2: The Catalyst]
Federal Reserve Policy Shifts (Rate Hikes / Quantitative Tightening)
[Phase 3: The Transmission Line]
Cross-Border Interbank Contraction => Shrinkage of International Bank Leverage Capacity
This leads to:
Collateral Velocity Freeze => Hoarding of Pristine Safe Assets (T-Bills) in Repo Markets
[Phase 4: The Systemic Outcome]
Global Dollar Shortage (Acute Liquidity Squeeze & Emerging Market Currency Crises)
As Hyun Song Shin (2012) demonstrated, offshore dollars are not generated by the Federal Reserve; they are generated by non-US global commercial banks expanding their leverage. When the Fed shifts its policy (raising interest rates or shrinking its balance sheet), it changes the cost of capital for these international banks. Because their funding costs rise, these banks are forced to contract their balance sheets, pulling back on cross-border lending.
The shortage emerges because these offshore dollars are created through bank lending. When global risk rises, or when the Federal Reserve tightens domestic monetary policy (as seen in recent macroeconomic cycles), international banks shrink their balance sheets and stop lending dollars.
Because international entities must constantly acquire dollars to service their existing debts and settle invoices, a sudden freeze in bank lending causes an acute liquidity squeeze. Corporations and states find themselves competing for a scarce pool of circulating dollars (Shin, 2012).
Modern offshore dollar creation does not happen via unsecured loans; it happens in the repo (repurchase agreement) market where cash is exchanged for US Treasuries (Pozsar, 2020).
When the Fed tightens policy or market risk rises, international institutions begin hoarding US Treasuries as a safety buffer. This hoarding slows down the “velocity” of collateral. Without available Treasuries to pledge, offshore banks can no longer secure dollar funding, accelerating the liquidity squeeze.
Safe Asset Scarcity Creates the Collateral Chokepoint
A modern dollar shortage is fundamentally a shortage of pristine collateral. In the wholesale financial plumbing—specifically the repurchase agreement (repo) markets—banks do not lend money unsecured. They exchange cash for high-quality collateral, primarily US Treasury bills (Gorton & Ordoñez, 2014).
As analyzed by financial strategist Michael J. Howell, global liquidity is highly dependent on the availability of these “safe assets.” When market volatility spikes, the financial system experiences a flight to safety.
International institutions hoard US Treasuries, locking them away on balance sheets. This hoarding causes a structural freeze in the velocity of collateral. Without available T-bills to pledge in the repo market, offshore banks cannot generate the dollar liquidity required to fund global business operations, starving the international system of its primary transactional lubricant (Caballero et al., 2017).
The structural scarcity of global dollars creates severe distortionary pressures across the international economic landscape.
Emerging Market Devaluations and Capital Flight
When dollars become scarce, their value surges relative to local currencies. To secure the dollars needed to pay foreign debts and buy imported food or energy, emerging market actors must aggressively sell their local currencies to buy USD. This triggers rapid capital flight and currency devaluations. Local central banks are then forced to burn through their foreign exchange reserves or raise domestic interest rates into a recession to defend their currencies (Rey, 2013).
The Triffin Dilemma Revisited
The system illustrates a modernized version of the Triffin Dilemma, the realization that the Bretton Woods gold-dollar standard was an inadequate foundation for a global monetary system.[2] To provide the world with the dollars needed for global trade, the US must run persistent current account deficits, exporting dollars to the rest of the world.
However, if the US tightens its spending belt or restricts capital outflows, the global supply of dollars dries up, causing international trade to contract. The world is trapped inside an architecture where the domestic policy needs of the United States directly conflict with the liquidity requirements of the global economy (Pozsar, 2020).
Supply Chain Contraction
Most global trade is intermediated through letters of credit and trade finance agreements structured in USD. When dollar funding costs spike due to offshore scarcity, banks slash their trade finance portfolios. A merchant in an emerging economy can no longer secure the short-term dollar loans required to clear a container ship, causing real-world supply chains to stall and dragging down global GDP growth (Amiti & Weinstein, 2011).
Academic and Institutional Perspectives
The structural reality of the dollar shortage is well-documented across elite macroeconomic institutions. The academic and institutional consensus surrounding the global dollar shortage is anchored by three distinct theoretical lenses that collectively expose the rigid plumbing of our international monetary system. Rather than viewing the dollar as a passive neutral unit, these scholars demonstrate how the greenback operates as an active, performative engine that dictates global economic boundaries.
The foundational boundary of this system is diagnosed by Hélène Rey (2013) through her seminal work on the Global Financial Cycle. Rey shatters a long-held economic myth, the idea that countries can protect their domestic economies from external shocks simply by letting their exchange rates float freely.
Instead, she demonstrated that the Federal Reserve’s monetary policy effectively dictates global leverage and credit conditions. When the Fed tightens its belt, it triggers a contractionary wave that strips independent monetary sovereignty away from other nations, regardless of their local currency arrangements, turning the global financial landscape into an direct reflection of U.S. domestic policy.
Where Rey outlines the macro-level cycle, Hyun Song Shin and researchers at the Bank for International Settlements (2012) expose the operational transmission lines through their analysis of the Global Banking Glut.
Shin shifts the analytical focus to the major international commercial banks that borrow and lend heavily in USD. These institutions act as the global transmitters of liquidity. When market volatility spikes or regulatory capital requirements stiffen, these banks face an immediate squeeze on their leverage capacity. The moment their ability to expand their balance sheets shrinks, a systemic, offshore dollar shortage automatically manifests, starving global businesses of the short-term credit lines required to sustain international commerce.
Finally, Zoltan Pozsar (2020) maps the highly specialized micro-mechanics of this system by examining Financial Plumbing and Repo Markets. Pozsar highlights a critical structural disconnection. The offshore Eurodollar system has generated a massive, self-reinforcing structural demand for dollars that cannot be satisfied by traditional domestic interventions like the Fed’s quantitative easing.
Because offshore interbank lending relies entirely on a continuous, highly fluid supply of pristine collateral, specifically US Treasury bills, any disruption or hoarding of these safe assets inside the repo market instantly causes severe funding bottlenecks.
Together, these three perspectives reveal a totalizing infrastructure where international liquidity is perpetually vulnerable to the structural limits of US debt and central bank coordination.
Tying your structural frameworks together clarifies the next phase of international political economy. The world’s recurring dollar shortages are structural symptoms of spreadsheet capitalism, an era where global economic relationships are passively modeled in isolated institutional grids and settled through a single nation’s balance sheet.
The convergence of the SACT framework and Artificial Intelligence shifts the international monetary system into a state of active, automated coordination.
By transforming Keynes’s institutional blueprints into an operational digital engine, the SACT-AI platform over time removes the geopolitical friction, banking chokepoints, and systemic scarcities of the dollar standard. It proves that the ultimate future of global liquidity belongs not to an single national currency, but to an automated, decentralized framework of synchronized global balance sheets.
Conclusion
When evaluated through a systemic lens, the global dollar shortage demonstrates the intense performativity of our international monetary architecture. The global financial system does not use the dollar as a passive camera to measure value; the dollar functions as an active engine that dictates the limits of global production.
Because the world has structurally transitioned into a totalizing dollar-denominated debt matrix, any pause in the creation or circulation of offshore dollars immediately transforms into a real-world economic contraction, proving that the plumbing of our monetary infrastructure remains the primary governor of global economic reality.
References
Amiti, M., & Weinstein, D. E. (2011). Exports and financial shocks. The Quarterly Journal of Economics, 126(4), 1841–1877.
Caballero, R. J., Farhi, E., & Gourinchas, P. O. (2017). The safe assets shortage conundrum. Journal of Economic Perspectives, 31(3), 29–46.
Gorton, G., & Ordoñez, G. (2014). Collateral crises. American Economic Review, 104(2), 343–378.
McCauley, R. N., McGuire, P., & Sushko, V. (2015). Global dollar credit: Links to US monetary policy and leverage. Economic Policy, 30(82), 187–229.
Pozsar, Z. (2020). Global Liquidity and the Funding Markets. Credit Suisse Economics Research.
Rey, H. (2013). Dilemma not trilemma: The global financial cycle and monetary policy independence. Proceedings of the Federal Reserve Bank of Kansas City Economic Symposium at Jackson Hole, 1–51.
Shin, H. S. (2012). Global banking glut and loan risk premium. Mundell-Fleming Lecture, IMF Economic Review, 60(2), 155–192.
Notes
[1] A serendipitous line from my work on spreadsheet logic and grammars.
[2] The Triffin dilemma was identified in the 1960s by Belgian-American economist Robert Triffin. It refers to the conflict of economic interests between countries whose currencies serve as global reserve currencies and those in their economic sphere. Triffin pointed out that too many US dollars in the world could draw down US gold reserves at Fort Knox.
AI Prompt(s) Make the academic case that the world has a shortage of USD and it is causing economic problems. Include references.
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Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations.
Anthony J. Pennings, PhD is a Professor at the Department of Technology and Society, State University of New York, Korea and a Research Professor for Stony Brook University. He teaches AI and broadband policy. From 2002-2012 he taught digital economics and information systems management at New York University. He also taught in the Digital Media MBA at St. Edwards University in Austin, Texas, where he lives when not in Korea.
Tags: Eurodollar Scarcity Paradox > pristine collateral > US Treasuries > USD Shortages
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Professor (full) at State University of New York (SUNY) Korea since 2016. Research Professor for Stony Brook University. Moved to Austin, Texas in August 2012 to join the Digital Media Management program at St. Edwards University. Spent the previous decade on the faculty at New York University teaching and researching information systems, digital economics, and global political economy
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