Pennings, A.J. (2026, Jan 19) Global Liquidity Theory and the SACT Layers. apennings.com https://apennings.com/technologies-of-meaning/global-liquidity-theory-and-the-sact-layers/

Introduction

How does financial liquidity impact financial markets, asset prices, and geopolitical stability in the emerging context of an AI-driven political economy? Using my SACT (Substitution, Abstraction, Symbolic Computing, and Telecommunications Synchronization) layers framework, I’ll explore how Michael Howell defines and interprets global liquidity in his Capital Wars (2020), and how this liquidity impacts financial markets, asset prices, and geopolitical stability.[1]

Howell argues that modern financial markets are essentially debt-refinancing machines driven by liquidity (cash) rather than by real investment or fundamentals. In his view, asset prices (P) depend on the level of Global Liquidity (L) and investors’ risk positioning, not on traditional valuation metrics. He even encapsulates this as P = L × (P/L), meaning that a rise in liquidity proportionally inflates financial prices. This AI-produced image is suggestive of the processes that drive liquidity and as a consequence, asset values.

Howell notes that today’s global liquidity pool is on the order of $130 trillion, roughly two-thirds the size of world GDP. A policy of monetary easing tends to create asset bubbles (in stocks, bonds, real estate) while “high street” inflation stays low. In short, feeding the money-creation “engine” drives prices of existing assets skyward, but not consumer prices.

Howell measures liquidity as the aggregate balance-sheet capacity of the financial system. Liquidity includes not only central bank money but also the vast “shadow” funding markets (repos, commercial paper, eurodollars, etc.) that amplify credit. Because roughly 80% of lending is collateral-backed, crises in his framework come from collateral shortages, not mispricing.

When liquidity tightens (for example, when bond or MBS prices fall), margin calls trigger fire sales that cascade through the system. Thus, financial instability is an endogenous feedback loop: falling liquidity causes plummeting collateral values, which in turn trigger further liquidity withdrawals and forced selling. In this view, systemic risk is systemic. It is nondiversifiable. Credit and collateral chains bind all institutions together, so only broad “backstops” can arrest a collapse.
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Central banks play a key role in Howell’s theory as market backstops. He emphasizes that modern central banking is largely about the plumbing. It is about providing collateral support, not just setting interest rates. For example, the Fed’s quantitative easing (QE), repo operations and FX swap lines act to replenish cash and collateral in crisis. It acts as a Dealer of Last Resort for the system, providing liquidity to financial institutions that are experiencing difficulties.

Because the US dollar dominates global finance, the Federal Reserve effectively functions as the world’s lender of last resort. Empirical tests confirm that Fed liquidity expansions quickly ripple into asset markets. For instance, Howell finds the Fed’s balance sheet moves lead Bitcoin and other markets by several weeks. Conversely, Fed-driven dollar shortages (as in 2008 or 2020) trigger global “risk-off” episodes, since roughly 60% of world trade is dollar-denominated.

In Howell’s framework, therefore, liquidity expansions fuel booms (often inflating collateral values), while liquidity contractions precipitate busts via frozen funding and margin spirals.

Liquidity and the SACT Layers of Spreadsheet Capitalism

Howell’s liquidity paradigm can be linked to the SACT (Substitution–Abstraction–Symbolic Computing–Telecommunications) stack of “spreadsheet capitalism,” which describes how finance is digitized in layers starting with the substitution of the world’s items and processes, decontextualizes them so they can be categorized, and computes them in various formulas, and synchronizes the fruits of those calculations globally.

Substitution

Illiquid economic value is substituted by paper or digital claims on spreadsheets. For example, mortgages, loans or real assets are pooled and securitized or tokenized onto ledgers, converting them into tradable securities. In this way liquidity literally substitutes for real collateral. As Vladimir Gorshkov at State Street observes, “in tokenized form, assets are easier to move, and hence easier to trade, transfer, lend and borrow.”

Howell’s liquidity logic relies on this mechanism. By transforming fixed claims into liquid instruments, the global balance sheet expands, allowing new funding flows.

Abstraction

These liquid claims and flows are then quantified in spreadsheets and models. Liquidity shows up as numeric inputs in performance metrics, risk models or spreadsheets (for example, as global money?supply indices, safe-asset supplies or repo rates). In Howell’s terms, central banks and analysts track the financial sector balance-sheet capacity as a key variable.

The SACT “stack” literally turns the world into rows and columns of numbers. Modern trading platforms use formulas (NPV, VaR, FX conversions, etc.) to translate economic positions into cell values. In practice, global liquidity indices and spreadsheet models compute how much cash and collateral is available, feeding those figures into algorithms that value portfolios and assess risk.[2]

Symbolic Computing

Added to the spreadsheet layout are automated algorithms and AI. These “symbolic processors” (quant trading programs, risk engines, AI forecasters) ingest the liquidity data and act on it. For instance, a trading algorithm might use a VaR model (built on liquidity inputs) to rebalance a bond fund, or an AI “robo-treasurer” might forecast funding needs from a policy change. Howell’s own work highlights this: tools like BlackRock’s Aladdin or even private AI are constantly interpreting liquidity as numeric signals.

The result is fully automated “intragroup” liquidity movements in seconds, tightening hedges and delivering real-time treasury functions. This exemplifies the symbolic layer where algorithms turn liquidity numbers into trading decisions.
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Telecommunications Synchronization

Finally, high-speed networks synchronize markets around those numbers. Real-time data feeds tie global exchanges and terminals into one grid. As Pennings notes, the USD’s hegemony is reinforced by a “networked grid of financial terminals” that run these spreadsheets simultaneously worldwide.[2]

In effect, liquidity shocks propagate instantaneously through this telecom layer. For example, a Fed liquidity injection in New York is immediately reflected in price quotes in London and Hong Kong. Howell’s theory assumes such synchronization. When global liquidity rises or falls, markets from equities to foreign exchange move in unison, reflecting the connected spreadsheet infrastructure.

Toward an AI-Driven Political Economy

Looking ahead, these trends suggest a transition to an economy where liquidity is managed by code as much as by policy. Central banks and firms are increasingly treating money and assets as programmable, real?time instruments. For example, major financial institutions are issuing tokenized deposits and stablecoins that settle 24/7. One notable project combines 24/7 blockchain rails with tokenized bank deposits and a large AI model to create a global treasury network.

Stablecoins and tokenized U.S. Treasuries are already handling trillions of dollars in global payments (Tether/USDC move approximately $33 trillion annually). In parallel, central banks recognize that AI will reshape policy. The BIS urges that regulators must “anticipate AI’s effects” on markets and harness AI tools in their own operations.
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Conclusion

The convergence of Howell’s liquidity view and the SACT framework points to movement towards an automated, AI-governed finance where liquidity flows are tracked and even managed by algorithms (big-data stress tests, smart contracts, real-time token-ledgers) rather than solely by traditional politics.

Economies that have been operationalizing the SACT layers will move to continuous, machine-enforced “balance sheets” where every dollar or digital token is coded and monitored. This suggests a new political economy in which policy and markets are coded into the very fabric of the globalized financial spreadsheet terminals, requiring us to think of liquidity as a computational resource (subject to AI control) as much as a purely political one.

Summary

The blog post examines the intersection of Michael Howell’s financial theories and the SACT framework of Spreadsheet Capitalism to explain how global markets function, and how they are transitioning to an AI-driven era. It begins by presenting Howell’s core argument from his book Capital Wars which posits that modern financial markets are no longer driven by traditional investment or fundamentals but are instead giant debt-refinancing machines fueled contingently by liquidity.

In this view asset prices are determined mathematically by the level of global liquidity and the risk positioning of investors rather than by the actual value of the underlying assets. Howell encapsulates this relationship in a formula where a rise in liquidity proportionally inflates market prices. He notes that the global liquidity pool has grown to roughly $130 trillion which is significantly larger than the world GDP. Consequently, when central banks engage in monetary easing they tend to create asset bubbles in stocks and real estate rather than driving up consumer prices on the “high street.”

The narrative explains that Howell measures liquidity as the aggregate balance-sheet capacity of the financial system including both central bank money and the vast shadow funding markets. Because the majority of lending is backed by collateral, financial crises in this framework are caused by collateral shortages rather than mispricing. When liquidity tightens and collateral values fall it triggers a feedback loop of margin calls and forced selling that can only be stopped by central banks acting as the dealer of last resort.

The post then maps this liquidity paradigm onto the SACT layers of Spreadsheet Capitalism to show how the physical world is converted into financial data. It starts with Substitution where illiquid real-world items like mortgages or land are substituted by paper or digital claims. This legal process transforms physical objects into tradable liquid instruments that expand the global balance sheet. Next is Abstraction where these claims are decontextualized into spreadsheet cells and tracked as numeric inputs in risk models. This turns the complex world into simple rows and columns of data that can be processed by financial algorithms.

The process accelerates in the Symbolic Computing layer where automated algorithms and increasingly AI ingest this liquidity data to make trading decisions without human intervention. Finally the Telecommunications Synchronization layer links these disparate spreadsheets into a single unified grid. High-speed networks ensure that a liquidity shock in New York is felt instantly in London or Hong Kong as the entire system moves in unison. Programs like BlackRock’s Aladdin use these inputs to move capital instantly across the globe.

The summary concludes by looking toward an AI-driven political economy where liquidity is increasingly managed by code. As programmable money and smart contracts become more prevalent, the global economy is transitioning to a system of machine-enforced balance sheets. In this future, financial stability becomes a matter of software engineering and algorithmic governance as much as political policy.

Notes

[1] Most insights on geopolitical stability and financial liquidity are from Michael Howell, CEO of Crossborder Capital, and his book Capital Wars: The Rise of Global Liquidity (2020), focusing on the global balance sheet.
[2] From my talk to the Computer Science Department at SUNY Korea on Friday, October 24, 2025.
[3]The SACT Layers of Spreadsheet Capitalism include Substitution (The Legal Layer), where illiquid real-world items (mortgages, land) are substituted by paper or digital claims (securities, tokens). This process turns physical collateral into tradable liquid instruments, expanding the balance sheet. Abstraction (The Financial Layer) is where claims are decontextualized into spreadsheet cells. Liquidity is tracked as numeric inputs in risk models (e.g., VaR) and indices, converting economic positions into raw data. Symbolic Computing (The Algorithmic Layer) involves algorithms (like BlackRock’s Aladdin) that ingest this liquidity data to formulate trading and other financial decisions. “Symbolic processors” turn liquidity numbers into instant capital movements without human intervention. Telecommunications (The Global Synchronization Layer) involves high-speed networks that synchronize these spreadsheets globally. A liquidity shock in New York propagates instantly to London or Hong Kong via the “networked grid of financial terminals.”

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Not to be considered financial advice.

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

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Category: artificial intelligence, enabling frameworks, enterprise systems, financial technology, spreadsheet logic, sustainable development, technologies of meaning
Tags: Abstraction > balance sheet > Global Liquidity > SACT (Substitution - Abstraction - Symbolic Computing - Telecommunications Synchronization) > Symbolic computing

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