Pennings, A.J. (2026, Oct 02) Dollar Liquidity, Stablecoins, and the Computational Foundations of AI4D: Spreadsheet Logic Across the Global Tiers. apennings.com https://apennings.com/digital-coordination/dollar-liquidity-stablecoins-and-the-computational-foundations-of-ai4d-spreadsheet-logic-across-the-global-tiers/

Introduction

In this post, the central argument treats spreadsheet logic as the computational layer between dollar liquidity and AI capacity. The tiers then become not simply levels of monetary access, but levels of access to the systems that convert liquidity into calculable assets, investment decisions, infrastructure and eventually AI-mediated action.

The global USD infrastructure can be described as a monetary hierarchy. At its center is the United States, followed by international financial centers, surplus exporters, emerging economies and countries with limited access to global finance. But there is another way to understand this hierarchy. The tiers can be seen as a gradient of access to the financial/computational infrastructure that makes dollar liquidity economically actionable.

This is where a return to spreadsheet logic becomes central to global AI for Development, or AI4D. Spreadsheet logic is much larger than Microsoft Excel or Google Sheets. Those applications made its basic grammar ubiquitous: economic information is substituted into cells, organized into tables, connected through formulas and recalculated as values change.

But this logic subsequently became embedded in the institutional infrastructure of global finance. Bloomberg Terminal, LSEG Workspace, China’s Wind Financial Terminal and BlackRock’s Aladdin all turn vast quantities of financial information into structured computational objects that can be compared, modeled, priced, stress-tested and connected to decisions. Bloomberg, for example, integrates market data, analytics, portfolio tools, APIs and agentic AI into its financial workflows; LSEG’s Workspace is adding natural-language interaction with its financial data and analytics; Wind explicitly connects its databases to Excel and APIs; and Aladdin describes its platform as a common data language linking portfolio management, data and risk analytics.

What began as a desktop technology has therefore become a form of financial computation at network scale.

The dollar is central to this system because of its exceptional liquidity and network effects. The Bank for International Settlements reported that the U.S. dollar was on one side of 89.2 percent of all foreign-exchange transactions in April 2025. The IMF reported that the dollar represented 56.77 percent of allocated global official foreign-exchange reserves at the end of 2025. The dollar thus provides not simply a currency but a widely shared unit of account, settlement and financial valuation.

Yet liquidity alone does not make an economy work. Someone must calculate the value of a Treasury security, the risk of a loan, the expected return on an infrastructure project, the cost of foreign borrowing, the exposure of a portfolio or the consequences of an exchange-rate movement. Spreadsheet logic performs this translation from financial value into computationally manipulable form.

That is why the five tiers can also be understood as five levels of access to computational financial capacity.

Tier 1: The US Core

At Tier 1, the United States combines dollar issuance with Federal Reserve liquidity, the Treasury market, domestic dollar funding and the institutions that regulate the core financial system. It also concentrates an extraordinary share of the financial and technological infrastructure required for advanced AI.

The important feature is the connection among these systems.
The Treasury issues debt. Financial-data systems represent its securities. Spreadsheets and databases model their yields, durations and cash flows. Portfolio systems incorporate them into institutional balance sheets. Risk engines stress-test them. Trading systems price and exchange them. Cloud systems process the data. AI systems increasingly interpret it.

At the US core, these processes reinforce each other. Deep financial markets make capital available; computational systems make that capital calculable; computing infrastructure makes AI possible. AI creates new demand for capital, electricity, data centers and advanced semiconductors. The World Bank’s 2025 AI-readiness framework captures part of this problem through its “four Cs”: connectivity, compute, context and competency. The United States is unusually well positioned across all four.

Tier 2: Global Financial Hubs

At Tier 2, global financial centers such as the United Kingdom, Singapore, Switzerland, Japan and the United Arab Emirates participate deeply in dollar and Eurodollar markets by lending out the world’s dominant reserve currency in exchange for good collateral. Their advantage is not merely access to dollars, it is access to highly sophisticated computational systems operating on top of dollar liquidity.

Banks, asset managers, corporations and trading firms use financial terminals, databases, analytics platforms, spreadsheets, APIs and portfolio systems to intermediate global capital. Bloomberg and LSEG illustrate this layer particularly well. Their systems connect market information, news, pricing, analytics, execution and increasingly AI-assisted workflows.

These financial centers therefore function as computational nodes in the dollar network. They convert information into prices, prices into risk assessments, risk assessments into portfolio allocations and portfolio allocations into transactions.

Their vulnerability appears when dollar funding becomes scarce. A financial model can remain computationally perfect while the underlying liquidity becomes more expensive. Spreadsheet logic can calculate the problem, but it cannot manufacture the dollar liquidity that the model assumes.

Tier 3: Surplus Exporters

Tier 3 includes major surplus exporters such as China and the Gulf states, which can accumulate foreign-exchange reserves, purchase US Treasuries and finance substantial international activity. These economies have a particularly interesting relationship to spreadsheet logic because they possess enough financial resources to build substantial computational capacity while still depending on global markets and imported technologies.

A central bank can represent its reserves as a portfolio. A sovereign wealth fund can model expected returns. An energy exporter can model commodity-price scenarios. A corporation can calculate foreign-exchange exposure. A government can model the effect of changes in U.S. interest rates on debt service and capital flows. The country thus turns accumulated dollar wealth into computationally organized investment capacity.

This is also where Wind is revealing. Wind does not simply reproduce an American spreadsheet environment. It provides a Chinese financial-data and analytics infrastructure while explicitly connecting its databases to Excel and APIs. The underlying spreadsheet grammar can therefore travel across different national financial systems.

That matters for monetary diversification. A country can build alternative payment rails or accumulate other currencies while continuing to use dollar-denominated assets because the computational and liquidity advantages of the existing system remain valuable. The issue is consequently not simply whether China or another surplus exporter can replace the dollar. It is whether an alternative can reproduce the liquidity plus the computational infrastructure that currently surrounds it.

Tier 4: Emerging Economies

At Tier 4, countries such as India, Brazil, Mexico, Indonesia and Türkiye receive substantial benefits from dollars through trade, investment, remittances, foreign borrowing and participation in global financial markets. But they face greater exposure to currency depreciation, sudden stops, higher external financing costs and limited access to advanced computing infrastructure.

Here spreadsheet logic becomes much more uneven. A business may have Excel or Google Sheets but lack access to sophisticated market data. A fintech company may use cloud services without possessing local compute infrastructure. A bank may access international financial markets but face higher funding costs. A government may be able to model an infrastructure project but lack affordable financing to build it. The important distinction is therefore between having computational tools and having computational capacity.

This is precisely the problem highlighted by the World Bank. AI participation depends upon connectivity, compute, context and competency, but those resources are distributed highly unevenly. High-income countries accounted for 77 percent of global co-location data-center capacity in June 2025, while low-income countries accounted for less than 0.1 percent.

Tier 4 therefore sits between financial participation and computational dependence.

Dollar liquidity can help a country purchase servers, cloud capacity, software and telecommunications equipment. Stablecoins can potentially reduce some payment and remittance frictions. Financial technology can connect businesses to global markets. But the country still needs the physical and organizational systems that turn financial access into productive computational capacity.

This is where AI4D becomes more than an AI-adoption program. It addresses the problem of building the ability to convert financial resources into computation, and subsequently in economic and social (education, food, health) development.

Tier 5: Peripheral and Low-Access Economies

At Tier 5, the separation between financial access and computational capacity becomes most dramatic. Countries with limited financial-market depth, weak institutions, inadequate electricity, poor broadband, small domestic markets or severe geopolitical constraints may have very little access to the deeper dollar infrastructure.

Yet digital platforms can potentially alter this relationship. A dollar-linked stablecoin can reach a mobile wallet even when a conventional correspondent-banking relationship is difficult or expensive. A mobile phone can reach a cloud service even when the country has no major data center. A locally developed AI application can use an external model even when domestic compute is scarce.

This creates a striking possibility. Financial representation can travel farther and faster than computational infrastructure. A dollar can reach a mobile phone before a data center reaches the country. That is precisely why stablecoins are important to the tier framework.

The GENIUS Act established a US regulatory framework for payment stablecoins, with an expected effective date of January 18, 2027 for its licensing provisions. Treasury Secretary Scott Bessent explicitly described stablecoins as an “Internet-native payment rail” and said that Treasuries backing stablecoins could generate additional demand for US government securities. The important qualification is that these are privately issued payment stablecoins subject to US regulation, not digital dollars issued directly by the US Treasury.

Stablecoins therefore potentially extend the dollar deeper into Tiers 4 and 5 while simultaneously extending the computational and regulatory infrastructure surrounding dollar-denominated value. But access to a digital dollar does not automatically produce AI capacity.

The IMF’s 2026 work on stablecoins demonstrates why. Stablecoin flows can affect foreign-exchange markets, local-currency values and dollar premia, while IMF analysis also warns that in economies with constrained access to foreign currency, greater stablecoin penetration can increase dollarization.

The development question consequently becomes more precise.
Can stablecoin liquidity be converted into productive computational capacity rather than simply into consumption, currency substitution, or financial circulation? That is the crucial Tier 5 AI4D problem.

Spreadsheet Logic as the Missing Middle Layer

The five tiers therefore reveal something that is often missing from discussions of AI development. AI4D is usually framed around infrastructure, data and skills. UN Trade and Development emphasizes those three leverage points, while the World Bank expands the framework to connectivity, compute, context and competency. These are essential, but they leave out an important mechanism, the computational organization of capital itself.

Spreadsheet logic provides that missing middle layer. It allows financial resources to become computational representations. Those representations become models. Models become forecasts. Forecasts become investment decisions. Investment decisions become infrastructure. Infrastructure creates computational capacity. Computational capacity enables AI. AI produces new data and new decisions. The loop then begins again. This can be expressed as:

Dollar liquidity -> financial data -> spreadsheet logic -> calculation -> capital allocation -> infrastructure -> AI -> action.

The significance of Bloomberg, LSEG, Wind and Aladdin is that they demonstrate how this logic scales beyond the individual spreadsheet. Excel and Google Sheets put computational finance into the hands of ordinary organizations. Financial terminals connect the logic to global markets. Portfolio platforms embed it into institutional decision-making. APIs connect it to other software. AI increasingly operates on top of the resulting data and workflows.

Spreadsheet logic has consequently become a kind of grid for global financial intelligence.

From Dollar Infrastructure to AI Infrastructure

This leads to a larger interpretation of the five-tier model.
Tier 1 has the greatest integration of liquidity, financial computation and AI infrastructure. Tier 2 has sophisticated financial computation operating on deep global liquidity.
Tier 3 has substantial accumulated financial resources and increasingly sophisticated domestic computational systems.
Tier 4 has meaningful participation in dollar finance but more constrained computational infrastructure. Tier 5 may obtain digital access to dollar-denominated value without possessing the physical and computational infrastructure necessary to exploit that value fully.

The tiers therefore measure something deeper than monetary access. They measure the capacity to move from:

money -> information -> calculation -> coordination -> intelligence -> action.

This helps explain why the dollar’s international role remains so consequential. Its network is valuable not simply because dollars are accepted everywhere, but because an enormous computational infrastructure has grown around them.

The dollar supplies liquidity. Financial markets supply assets and prices. Spreadsheet logic organizes those assets and prices into calculable relationships. Financial information platforms/terminals distribute those relationships across institutions. Cloud and telecommunications systems distribute the computation. AI increasingly interprets the resulting data and participates in decision processes. The architecture is becoming increasingly integrated.

The AI4D Question

The most important AI4D question may therefore not be whether developing countries can obtain artificial intelligence. It is whether they can enter the financial-computational loop through which AI capacity is being built. A country that has dollar liquidity but no reliable electricity cannot easily turn money into compute. A country with broadband but little financial-data infrastructure may struggle to evaluate investment opportunities. A country with financial models but little local data may struggle to build contextually relevant AI. A country with AI software but no organizational capacity may struggle to turn model output into development outcomes.
And a country with a stablecoin wallet but no electricity, broadband, data, skills or institutional capacity may possess a representation of value without the infrastructure required to convert that value into development.

This is why spreadsheet logic belongs at the center of the framework. The spreadsheet is the historical technology that taught organizations to represent economic reality as a network of interdependent computational objects. Bloomberg, LSEG, Wind and Aladdin scaled that grammar into global financial infrastructure.

Stablecoins are beginning to place those representations of value into portable digital networks.

AI is now adding a new capacity: the ability to interpret, recombine and increasingly act upon these computational representations. The historical sequence can therefore be understood as an extention of my SACT (Substitution, Abstraction, Computation, Telecommunications) framework:

Ledger -> spreadsheet -> financial information system -> networked finance -> stablecoin -> AI.

Each stage increases the distance between the “messy world” of economic reality and the human act of calculation while increasing the ability of computation to coordinate economic activity.

For AI4D, this makes the five tiers especially revealing. The central issue is not simply who has dollars. It is who can convert dollars into computation, computation into intelligence, and intelligence into development. At the top of the system, these processes reinforce one another. Toward the periphery, they become increasingly disconnected.

The developmental challenge is therefore to close that gap. The objective of AI4D is not merely to distribute AI models. It is to build the financial, computational, infrastructural and organizational capacity required to turn global liquidity into local productive capability.

The dollar supplies much of the world’s liquidity. Spreadsheet logic makes that liquidity calculable. AI increasingly makes those calculations operative. AI4D asks who gets to participate in the entire chain.

References

Bank for International Settlements. (2025). OTC Foreign Exchange Turnover in April 2025. BIS.
BlackRock. (2026). Aladdin: Investment and Risk Management Technology. BlackRock.
Bloomberg Professional Services. (2026). Bloomberg Terminal. Bloomberg.
Verzunm, Evgen. (2024) BlackRock’s Aladdin: The Hidden Operating System of Global Finance. Linkedin post.
Bloomberg Professional Services. (2026). Portfolio & Risk Analytics. Bloomberg.
International Monetary Fund. (2026). Currency Composition of Official Foreign Exchange Reserves (COFER): Fourth quarter of 2025. IMF Data Brief.
International Monetary Fund. (2026). Stablecoin inflows and spillovers to FX markets. IMF Working Paper pdf 2026/056.
International Telecommunication Union. (2026). AI for Good 2026: My Key Takeaways. ITU.
LSEG. (2026). LSEG Workspace and AI. London Stock Exchange Group.
Treasury, U.S. Department. (2025). Statement from U.S. Secretary of the Treasury Scott Bessent on enactment of the GENIUS Act.
Treasury, U.S. Department of the. (2026). Treasury seeks public comment on GENIUS Act proposed rulemaking.
UN Trade and Development. (2025). Technology and Innovation Report 2025: Inclusive Artificial Intelligence for Development. UNCTAD.
Wind Information. (2026). Wind Financial Terminal. Wind Information.
World Bank. (2025). Digital Progress and Trends Report 2025: Strengthening AI Foundations. World Bank.

Notes

[1] The particularly interesting feature for the spreadsheet-logic argument is that these tiers can also be interpreted as different degrees of access to the computational infrastructure that makes dollar liquidity actionable.
[2] One conceptual formulation stands out as particularly useful for my larger project. The five tiers represent decreasing ability to turn monetary liquidity into computational agency. That puts my SACT/spreadsheet argument exactly between the dollar infrastructure and AI infrastructure. It explains the mechanism by which liquidity becomes representation, representation becomes calculation, calculation becomes allocation, and allocation becomes operative capacity.

AI Prompt(s) Write a version within the context of global AI4D that emphasizes the dollar’s international dominance understood not only as an American privilege imposed on the rest of the world, but as globally distributed economic benefit produced by liquidity, network effects, and low transaction costs. Add my work on spreadsheet logic and stablecoins. The paradox is that the United States can use the same infrastructure to restrict access when geopolitical relationships break down.

© ALL RIGHTS RESERVED

Not to be considered financial advice. AI is often used, and results are thoroughly interrogated. Links are used for some citations. Views are my own and do not express the stances of my employers, past or present.

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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: ai4d, artificial intelligence, data analytics and meaning, democratic political economies, digital coordination, financial technology, public administration, science and technology studies, smart new deal, spreadsheet logic, strategic communications, sustainable development
Tags: blockchain > US dollar stablecoins > USD-liquidity tiers

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