Pennings, A.J. (2026, Jul 29) Operative Mediation: From Representation to Coordination. apennings.com https://apennings.com/artificial-intelligence/operative-mediation-from-representation-to-coordination/

Introduction

One of the central arguments in my work is that the digital spreadsheet should not be understood merely as a business application or accounting tool. It introduced a new representational logic that transformed writing, numbers, lists, tables, cells, formulas, and functions into an integrated computational medium. This techno-epistemologic became the foundation of modern financial markets, organizational management, and, increasingly, artificial intelligence.

Yet spreadsheet logic alone cannot fully explain how contemporary computational systems operate. It primarily explains how representations are organized and computed. To understand today’s AI systems, blockchain networks, and automated financial infrastructures, we need a complementary theory that explains how representations become actions. I call this extension, operative mediation.

Operative mediation builds upon Bolter and Grusin’s (1999) theory of remediation but moves beyond their emphasis on representation. Remediation is a powerful theory that explains how new media absorb, refashion, and reorganize older media forms to create a more “authentic” representation of the real.[1] Television, for example, remediated radio and film, and later computer graphics and windows.

The digital spreadsheet, as I have show, remediated writing, accounting ledgers, lists, tables, and mathematical notation into a dynamic computational and visual grid. Artificial intelligence subsequently remediates spreadsheets, databases, search engines, and countless other digital media into statistical models capable of generating new content.

These are fundamentally representational processes. They explain how one medium becomes another. Operative mediation asks a different question: How do representations become coordinated actions?

To answer this question, operative mediation introduces a semiotic dimension drawn from Charles Sanders Peirce. Peirce argued that signs do not simply represent objects; they generate interpretants, responses that can produce further thought, communication, or action. Signs exist within an ongoing process of semiosis rather than as static representations.[2]

Spreadsheet logic provides the representational infrastructure for semiosis. A spreadsheet cell, for example, contains a sign that refers to financial assets, inventories, labor costs, or Treasury securities. Formulas relate these signs to one another through explicit computational rules. Traditional spreadsheet models stop there. They calculate.

Operative mediation extends this process by focusing on what Peirce called the energetic interpretant, the stage at which interpretation produces action. Rather than ending with a computed result, computational outputs trigger decisions, transactions, interventions, workflows, and automated behaviors. The spreadsheet ceases to be a descriptive model and becomes part of an operational infrastructure.

This extension becomes increasingly visible in contemporary financial systems. A liquidity model does not merely estimate funding requirements; it automatically reallocates capital. A fraud-detection algorithm does not merely identify suspicious transactions; it freezes accounts. A Treasury-backed stablecoin system does not merely record balances; it executes payments and synchronizes settlement across distributed networks. Representation transitions into coordination.

Operative mediation therefore adds a theory of action to spreadsheet logic. Spreadsheet logic explains how information is represented, organized, and computed. Operative mediation explains how those computational outputs become organizational behavior.

This distinction also suggests a third logic of remediation.

Bolter and Grusin identified two dominant logics that characterize new media. The first is immediacy, the attempt to erase awareness of the medium itself by creating transparent representations. The second is hypermediacy, where multiple media coexist visibly, emphasizing the layered nature of mediation. Operative mediation introduces a third logic that might be called operativity.

The objective is no longer simply to represent reality transparently or to multiply representations. Instead, the purpose of mediation becomes execution. Representations are connected directly to computational processes capable of producing real-world consequences. The medium increasingly functions as an engine rather than as a window.[2]

This perspective also clarifies the historical transition of computational media.

Writing preserved memory.

Accounting standardized economic relationships.

The spreadsheet computed organizational models.

Artificial intelligence evaluates patterns and generates predictions.

Operative mediation coordinates distributed action.

Seen this way, AI is not merely another representational medium. It inherits spreadsheet logic as its computational grammar while extending it into systems capable of organizing economic and institutional behavior. Models become agents. Predictions become interventions. Information becomes infrastructure.

This is where Donald MacKenzie’s notion of performativity becomes particularly relevant. In An Engine, Not a Camera (2006), MacKenzie argued that financial models do not simply describe markets, they actively shape them. Operative mediation generalizes this insight beyond financial economics. It proposes that computational media increasingly perform the realities they represent. Financial models allocate capital. Recommendation systems influence attention. Autonomous agents negotiate APIs. Stablecoin infrastructures coordinate liquidity. AI systems increasingly participate in the very environments they model.

Operative mediation therefore completes the transition that began with spreadsheet logic. The spreadsheet established a computational grammar for organizing representations. Operative mediation explains how those representations enter Peirce’s process of semiosis, generate energetic interpretants, and ultimately coordinate distributed action across organizations, markets, and digital infrastructures.

The trajectory is clear: representation becomes computation; computation becomes coordination; coordination becomes the defining function of the contemporary computational medium.

References

Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding New Media. MIT Press.
MacKenzie, D. (2006). An Engine, Not a Camera: How Financial Models Shape Markets. MIT Press.
Peirce, C. S. (1931–1958). Collected Papers of Charles Sanders Peirce (C. Hartshorne, P. Weiss, & A. W. Burks, Eds.). Harvard University Press.
Rose, J., & Pennings, A. J. (2022). Knowledge, decisions, and norms: A framework for studying the structuration of spreadsheets in social organizations. Information, 13(2), 46.
McLuhan, M. (1964). Understanding Media: The Extensions of Man. McGraw-Hill. MIT Press.
Giddens, A. (1984). The Constitution of Society. University of California Press.
Lyotard, J.-F. (1984). The Postmodern Condition: A Report on Knowledge. University of Minnesota Press.

Notes

[1] I’ve had an admittedly strange obsession with trying to understand and explain the power of digital spreadsheets.
[2] In An Engine, Not a Camera (2006), MacKenzie describes how economic models perform the economy. I expand this to describe how digital spreadsheet’s SACT organization perform the economy.
AI Prompt(s) Describe my theory of operative mediation for blog post. How does it add a theory action and semiosis to spreadsheet logic? How does it add a third logic of remediation?

© ALL RIGHTS RESERVED

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

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

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Category: artificial intelligence, blockchain

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