The TSM Graduate in the AI Workplace

Posted on | September 6, 2026 | No Comments

Pennings, A.J. (2026, Sep 06) The TSM Graduate in the AI Workplace. apennings.com https://apennings.com/digital-coordination/the-tsm-graduate-in-the-ai-workplace/

Introduction

This post frames one version of the TSM graduate as a systems manager who is technically fluent enough to work directly with AI and technical specialists, but whose primary responsibility is integrating technology with organizational objectives.

This changes the student’s conception of AI. They stop seeing AI as a chatbot or model and start seeing it as a networked computational infrastructure embedded in an organizational system.[1] That is precisely where TSM can distinguish itself from conventional computer science. CS students need to know how to build the computational components. TSM students need to understand how those components are assembled, governed, connected to people and resources, and converted into organizational capabilities and actions.

The key is not to turn the TSM graduate into a second-rate computer scientist. It is to develop a professional profile as someone who can understand technology deeply enough to direct it, understand organizations deeply enough to transform them, and understand AI deeply enough to work with it as a computational partner.

The TSM Graduate in the AI Workplace

A graduate entering the workplace with a degree in Technological Systems Management is entering an environment in which the boundary between management and technical work is becoming increasingly permeable. Artificial intelligence is making it possible for managers to participate directly in activities that once required specialized programmers and data engineers. At the same time, technical systems are becoming too complex to manage effectively without an understanding of organizations, strategy, economics, people, and institutional constraints.

The TSM graduate therefore occupies an important middle position. They are not simply a manager who happens to use technology. Nor are they simply a technologist who has learned management techniques. Their distinctive role is to connect organizational objectives with technological systems.

This becomes particularly important as AI changes the way software is produced. A manager can increasingly ask an AI system to construct a database, create a retrieval-augmented generation pipeline, develop an MCP server, connect an API, write code, test an application, or coordinate several specialized agents. The manager does not necessarily need to implement every component personally. But they do need to understand what those components do and how they fit together.

The TSM graduate therefore needs to develop three complementary capabilities: Technical literacy + Leadership capability + Management capability. These should not be treated as three separate areas of education. They should operate as an integrated professional system.

Technical Skills Mean Learning Enough to Build, Evaluate, and Direct

The first responsibility of the TSM graduate is technical competence. This does not mean mastering every programming language or becoming an expert in every AI framework. Technology changes too quickly for that to be a durable educational strategy. Instead, the TSM graduate should understand the architecture of contemporary computational systems.

They should be able to understand what happens when data enters a system, how it is stored and represented, how AI retrieves it, how models process it, how tools are connected, how agents coordinate, and how outputs become organizational actions.

A TSM graduate should therefore be comfortable with programming fundamentals, databases, APIs, cloud infrastructure, cybersecurity, data engineering, and systems architecture. They should also understand embeddings and vector databases, RAG pipelines, LLM APIs, tool calling, MCP servers, AI agents, and multi-agent orchestration.

The objective is not necessarily “I can build everything myself.” It is “I understand enough to determine what should be built, evaluate what AI has built, and work effectively with people who build it.” That distinction is critical.

A TSM graduate might use an AI coding system to construct an initial RAG application. They should nevertheless understand enough about embeddings, chunking, retrieval, metadata, reranking, provenance, security, and evaluation to recognize whether the resulting system is appropriate.

Likewise, they might ask AI to create an MCP server connecting an agent to an enterprise database. But they need to understand authorization, permissions, data exposure, logging, and failure modes before allowing that system into production. Technical competence thus becomes a form of managerial control.

The TSM Technical Stack

A useful progression for the TSM graduate is Programming -> Data -> Networks -> Cloud -> AI -> Agents -> Systems

– Programming provides the basic computational vocabulary.
– Data provides the material upon which organizations increasingly operate.
– Networks explain how computational resources communicate.
– Cloud infrastructure provides scalable computational capacity.
– AI provides increasingly capable computational reasoning and generation.
– Agents introduce planning and autonomous tool use.
– Systems thinking connects all of these components to organizational objectives.

The graduate does not need to become a specialist in every layer. They need to understand the interfaces between the layers. That may ultimately be one of the most valuable technical skills in the AI workplace.

Leadership Skills that Direct People and AI Together

The second capability is leadership. AI changes leadership because the manager increasingly has two kinds of collaborators, people and computational agents. A manager may eventually supervise a team in which several employees work alongside specialized AI systems. A researcher may have an AI research assistant. A financial analyst may work with an analytical agent. A software team may use coding agents. A marketing group may employ agents for customer analysis and content production. The leadership problem consequently becomes more complicated.

The manager must determine:
– What should humans do?
– What should AI do?
– Where should they work together?
– Where must humans retain authority?

This requires judgment rather than technical proficiency alone. A TSM graduate should become skilled at defining objectives, decomposing complex problems, assigning responsibilities, establishing decision rights, creating feedback mechanisms, resolving conflicts, and developing trust.

The manager’s job increasingly becomes one of orchestration.
This is an interesting parallel with multi-agent AI. A multi-agent system requires an architecture for distributing tasks among specialized agents. Organizations have always faced a similar problem with people.

The TSM graduate therefore needs to understand both. They should be able to ask “If this were an AI system, which agent would perform this task?” But also “Should this task actually be delegated to AI?” And “Who remains accountable for the result?” That is leadership in an AI-mediated organization.

Management Skills that Turn Technology Into Organizational Performance

The third capability is management. Technical systems have no organizational value simply because they are technologically sophisticated. Their value comes from what they enable an organization to accomplish.

The TSM graduate therefore needs to understand strategy, finance, operations, project management, organizational behavior, economics, risk, procurement, change management, and performance measurement. They need to translate between organizational language and technical language.

A senior executive might say “We need to reduce the cost of customer support by 30 percent.” A computer scientist might think “We need an LLM, RAG, vector database, and agent architecture.” The TSM graduate needs to bridge these statements. They should ask:

What is actually causing the cost?
What information does the organization possess?
Where are the bottlenecks?
Which processes are suitable for automation?
What should remain human?
What technology is required?
What will implementation cost?
How will performance be measured?
What risks are introduced?
How will employees respond?
And ultimately:

Does the system improve the organization’s ability to accomplish its objective? That is the management contribution.

The New Skill Is Integration

This suggests that TSM should not be organized around the idea that students need to become experts in either technology or management. They need to become integrators.

The TSM graduate should be able to move in both directions.

From management toward technology:

Strategy -> Process -> Data -> Technology -> Implementation

And from technology back toward management:

Technology -> Capability -> Organizational Change -> Performance -> Strategy

The graduate becomes the person who can see the entire system.

This is increasingly important because AI reduces the cost of moving from an idea to a working prototype. The scarce resource may therefore shift from coding capacity toward problem definition, organizational knowledge, judgment, integration, and accountability.

The Manager as an AI Systems Architect

This is where the earlier discussion of AI engineering becomes particularly relevant. A traditional software project might have looked like:

Manager -> Systems Analyst -> Programmer -> Application

The AI-mediated organization increasingly looks like:

Manager -> AI -> Computational System -> Organizational Action

The manager does not disappear from the process. In some respects, the manager becomes more important because the cost of specifying and constructing computational systems falls.

A TSM graduate might tell an AI system: “Analyze our customer-support process. Identify repetitive activities. Examine the relevant documents and historical tickets. Design a RAG system for our support staff. Connect it to the knowledge base through an MCP server. Develop an agent that drafts responses but requires human approval before sending them. Create an evaluation framework and report the results.”

That is no longer traditional management. But it is also not traditional programming. It is AI systems management.

The TSM graduate is specifying the architecture, allocating responsibility, establishing constraints, evaluating results, and connecting computational capability to organizational objectives.

From Prompting to Specification

This also changes what we should mean by “AI literacy.” Prompt engineering is useful, but it is not enough. The more durable skill is systems specification. A TSM graduate should be able to specify:

Objective -> Data -> Process -> Model -> Tools -> Agents -> Human Oversight -> Metrics -> Governance

This is much closer to engineering than simply asking an AI chatbot questions. The graduate should be able to create a system specification and then use AI to help implement it.

In this model, AI becomes something like a computational workforce operating under human-defined objectives and constraints. The manager becomes responsible for the architecture of that workforce.

TSM and the Future of Computer Science

This also clarifies the relationship between TSM and computer science. Computer science will continue to produce specialists who understand algorithms, operating systems, distributed computing, databases, networks, AI models, security, and computational architecture at considerable depth. TSM should produce professionals who understand enough of those systems to integrate them with organizations.

The distinction might be expressed simply:

Computer Scientists will ask “how do we build the computational system? TS Managers will ask “How do we use computational systems to transform the organization?” The outcome is AI-enabled TSM that asks “How do we manage people, AI, technology, and organizational resources as one integrated system?” That third question may become increasingly important.

A TSM Professional Development Model

For a graduate entering the workplace, I would organize their development around four questions.

1. Can I understand the technology?

Learn enough programming, databases, AI, cloud, cybersecurity, APIs, RAG, vector databases, MCP, and agents to understand what is technically possible.

2. Can I design the system?

Learn systems architecture, process analysis, requirements analysis, workflow design, data architecture, AI orchestration, evaluation, and governance.

3. Can I lead the people?

Learn communication, negotiation, organizational behavior, team leadership, conflict resolution, decision-making, and change management.

4. Can I produce organizational value?

Learn strategy, finance, operations, economics, project management, risk management, and performance measurement.

The four questions form a professional progression:

Understand -> Design -> Lead -> Deliver

That is a powerful way to think about the TSM graduate.

The TSM Graduate as a Boundary-Spanner

Ultimately, the TSM graduate’s greatest advantage may be the ability to operate across boundaries. They can talk with the computer scientist about architectures. They can talk with the data engineer about pipelines. They can talk with the AI team about agents and RAG. They can talk with executives about strategy. They can talk with employees about workflow. They can talk with finance about investment. They can talk with customers about value. And they can increasingly talk directly with AI systems about how computational work should be performed.

This makes TSM particularly relevant to the workplace emerging around AI. The goal is not to produce graduates who compete directly with computer scientists, data scientists, or business managers. It is to produce graduates who can connect these professions.

The TSM graduate becomes the person who understands that a vector database is not merely a database, that an MCP server is not merely an API, that an AI agent is not merely a chatbot, and that multi-agent orchestration is not merely a programming technique. Each is a component of an increasingly integrated technological-organizational system.

And that may be the central competency of TSM in the AI era:

The ability to understand, design, lead, and manage systems in which humans and computational agents work together to accomplish organizational objectives. The traditional computer scientist learned to make computers compute. The AI engineer is learning to make machines retrieve, reason, and act. The TSM graduate must learn something broader: How to organize the people, technologies, AI systems, resources, and decisions that make those capabilities useful.

That is not simply management with more technology. It is the emergence of a new form of technological systems management.

Notes

[1] A networked computational infrastructure links various computers, servers, and hardware accelerators through communication networks to share data, balance workloads, and run large-scale applications. It invoves physical or virtual machines like servers, GPUs, and client devices that process and store information. It interconnects high-speed network fabrics, switches, and routers that move data between nodes with low delay. It uses centralized or distributed data repositories connected via dedicated backend networks.
[2]
AI Prompt(s) Take those last few responses and shape them into the perspective of a TSM graduate going into the workplace. How should they organize their technical, leadership, and management skills?

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.

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.

Category: artificial intelligence, data analytics and meaning, digital coordination, enterprise systems, technology management
Tags: TSM

Legal and Regulatory Restrictions on Modern Monetary Theory (MMT)

Posted on | September 5, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Sep 05) Legal and Regulatory Restrictions on Modern Monetary Theory (MMT). apennings.com https://apennings.com/financial-technology/legal-and-regulatory-restrictions-on-modern-monetary-theory-mmt/

Introduction

In the age of climate volatility and baby boomer retirement, discovering sustainable paths to finance socially focused programs has become an urgent public priority. Modern Monetary Theory (MMT) offers an analytical perspective on public finance that highlights how national expenditure can advance full employment, accelerate decarbonization, and underwrite resilient public infrastructure. Stephanie Kelton (2020) directly challenged orthodox assumptions surrounding sovereign deficits, dispelling the persistent analogy that equates federal fiscal operations with the budgetary constraints of a private household.

This essay examines the analytical promise of MMT for the United States political economy while interrogating the statutory, legislative, and institutional mechanisms that restrict long-term fiscal expansion without corresponding taxes or bond auctions. While MMT demonstrates that sovereign currency issuers face resource constraints rather than financial ones, the US institutional architecture intentionally ties deficit outlays to debt issuance through Treasury auctions. The central structural barrier to MMT-style policy is not an operational inability to spend, but a web of historical statutes that mandate public debt borrowing alongside deficit outlays.

The Analytical Case for MMT

Kelton’s framework builds directly on Warren Mosler’s (1994) foundational treatise, Soft-Currency Economics. Grounded in direct trading floor experience within wholesale bond markets, Mosler demonstrated that a sovereign government issuing an unbacked fiat currency operates under entirely different fiscal realities than private market participants. As Mosler observed, fiat money operates essentially as a tax credit rather than a physical commodity.

Sovereign currency issuers cannot become involuntarily insolvent in their own unit of account. Government spending is not constrained by accumulated tax revenues or prior borrowing; rather, federal disbursements occur when the central bank credits private bank reserves, which injects fresh liquidity into the commercial banking system. Sovereign deficits directly generate the net financial assets of the private sector. Within this mechanics-first approach, the primary constraint on public spending is real resource capacity such as labor, raw materials, energy, and industrial infrastructure rather than nominal treasury balances. Overspending relative to real productive capacity generates demand-pull inflation, which represents the genuine operational boundary of fiscal policy.

Taxes perform a crucial macroeconomic function in this system. They drive demand for the sovereign currency and drain purchasing power from circulation to mitigate inflationary pressures. Taxes can also disincentivize socially costly activities or steer capital investment, as seen with the clean-energy tax incentives established under the Inflation Reduction Act of 2022 (P.L. 117-169). Concurrently, public bond sales do not fund government operations in a mechanical sense; instead, they serve as liquidity-draining operations that allow the central bank to manage policy interest rates while supplying international capital markets with risk-free collateral. High-quality liquid assets like US Treasuries are foundational to offshore credit intermediation and Eurodollar markets.

Crisis Fiscal Policy and the Pandemic Spending Precedent

The federal response to the COVID-19 pandemic served as a massive empirical stress test of sovereign fiscal capacity. Interventions authorized under both the Trump and Biden administrations deployed trillions of dollars to backstop the domestic and global economies. Programs like the Paycheck Protection Program, authorized by the Coronavirus Aid, Relief, and Economic Security (CARES) Act (P.L. 116-136), injected unprecedented liquidity directly into private balance sheets, demonstrating how quickly a currency issuer can mobilize purchasing power during an emergency.

However, because these expenditures were recorded under traditional budget frameworks, the nominal federal debt tally expanded rapidly. This expansion revitalized orthodox political narratives warning of imminent insolvency, fiscal decline, and generational debt burdens—tropes frequently amplified by hard-money and cryptocurrency advocates promoting speculative hedges against sovereign fiat.

From an institutional perspective, the pandemic highlighted several unresolved questions:

What institutional indicators define the real capacity limits of the US productive base? What are the limits, if any, to US government spending?

Can public disbursements ever be legislatively severed from nominal debt tracking? Can US spending be severed from current debt accounting metrics?

Which democratic frameworks should prioritize public capital investments? What are the optimum spending choices for additional spending?

How can a state prevent fiscal extraction by political factions seeking to co-opt sovereign currency creation for narrow private interests? Can nefarious administrations coopt the MMT analysis and increase spending on various projects unassociated with democratic political economy?

The Architecture of the US Federal Spending Apparatus

The US Constitution vests Congress with the structural authority to coin money and regulate its value (U.S. Const. art. I, § 8, cl. 5). The creation of the Department of the Treasury in 1789 initiated an institutional infrastructure to administer federal disbursements, tax receipts, and public credit operations.

Modern fiscal mechanics operate primarily through the Treasury General Account (TGA) maintained at the Federal Reserve Bank of New York. The Federal Reserve Act of 1913 formally established the central bank as the government’s fiscal agent. Whenever the Treasury disburses funds, the Federal Reserve credits the reserve account of the recipient’s depository institution while debiting the TGA.

Crucially, modern statutory practice prohibits the Treasury from running indefinite, unbacked overdrafts on the TGA. While the Federal Reserve historically permitted temporary Treasury overdrafts, legislative modifications ended this flexibility by the early 1980s, mandating that the TGA maintain positive collected balances sourced strictly from tax receipts or authorized security sales. Within the Treasury, the Bureau of the Fiscal Service coordinates these daily inflows and debt issuances through cash forecasting and liquidity auctions.

Statutory Barriers that Mandate Debt Issuance for Deficit Spending

The legal mandate requiring the Treasury to issue interest-bearing debt to cover revenue shortfalls is codified across several landmark statutes:

The Second Liberty Bond Act of 1917 was enacted during World War I, this statute and its subsequent revisions granted the Treasury broad authority to issue bonds, notes, and certificates of indebtedness, while establishing an aggregate statutory debt ceiling that continues to trigger recurring legislative standoffs.

Section 14 of the Federal Reserve Act delineates the open-market operations of the Federal Reserve Banks and strictly prohibits the central bank from purchasing newly issued securities directly from the Treasury. By restricting the Fed to secondary-market transactions, the statute prevents direct debt monetization and forces the Treasury into private dealer auctions.

The Treasury-Federal Reserve Accord of 1951 formally ended the wartime policy of pegging Treasury bond yields, establishing operational independence between the Federal Reserve’s monetary targets and the Treasury’s fiscal financing operations.

Title 31 of the United States Code was set into positive law in 1982, Title 31 (§ 3101–3121) governs federal money, debt administration, and public borrowing operations. Section 3121 explicitly directs the Secretary of the Treasury to manage public debt through competitive market auctions, reinforcing market-based investor absorption as the required counterpart to deficit spending.

These statutes collectively enforce an institutional barrier. Even if the sovereign state of the US faces no intrinsic currency constraint, its administrative agencies are legally barred from spending beyond tax receipts without selling an equivalent volume of debt instruments into the financial markets.

From Reaganomics Spending to the Clinton Surpluses

The modern configuration of this debt-issuing apparatus matured during the 1980s. The Reagan administration pursued supply-side tax cuts through the Economic Recovery Tax Act of 1981 (P.L. 97-34) and the Tax Reform Act of 1986 (P.L. 99-514), coupled with significant defense expenditures. Because regulatory statutes prohibited direct treasury monetization, these deficits necessitated a major expansion of computerized, open-market Treasury debt auctions. Rather than sparking domestic capital investment alone, these structural shifts stimulated internationalized capital flows, setting up persistent current account deficits and broad industrial off-shoring.

During the 1990s, the Clinton administration pursued fiscal consolidation. Supported by revenues from the Omnibus Budget Reconciliation Act of 1993 (P.L. 103-66), rapid productivity gains from the commercial Internet expansion, and military spending reductions following the Cold War, the US federal government posted consecutive unified budget surpluses from fiscal years 1998 through 2001.[1]

From an MMT accounting perspective, persistent sovereign surpluses necessarily drain net financial assets from the domestic private sector unless offset by external trade surpluses. As private liquidity contracted and domestic debt expanded to sustain consumption, the economy proved vulnerable to the 2000–2002 dot.com and telecom technology busts. The subsequent Bush administration rapidly reversed these surpluses through the Economic Growth and Tax Relief Reconciliation Act of 2001 (P.L. 107-16), the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (P.L. 108-173), and prolonged military engagements in Iraq and Afghanistan, which cost trillions of dollars over two decades (Crawford, 2021).[2]

Structural Limitations and Political Economy

The analytical insights of MMT accurately illuminate the operational mechanics of fiat currency. A sovereign issuer creates money through expenditure and uses bond sales and taxation to stabilize reserves, manage interest rate targets, and curb excess purchasing power. Nevertheless, deploying this framework within the American political economy requires addressing deep-seated legal, institutional, and inflation risks:

Supply-Side bottlenecks and inflation, as demonstrated during the post-pandemic recovery, indicated significant inflation efects from supply chain limitations. After injecting signicant sovereign liquidity into an economy constrained by brittle supply chains, geopolitical energy shocks, and concentrated market power, price inflation emerged. This even occured when labor markets are not fully saturated.

Legal and regulatory lock-in limits spending authorities. The statutory framework embedded in Title 31 Code, the Federal Reserve Act, and the Second Liberty Bond Act legally prevents the executive branch from bypassing private bond dealer auctions. Reforming these structures would require contentious congressional legislation.

Political capture and allocation risk pressure sovereign states with an unconstrained fiscal pipelines. They face intense pressure from organized corporate and defense lobbies to direct spending toward subsidized private rents rather than public infrastructure, human capital, or climate adaptation.

Unlocking sovereign public spending to confront systemic crises, from decarbonization to demographic aging, demands more than theoretical clarity regarding fiat monetary mechanics. It requires democratically reforming the legislative covenants and statutory hurdles that continue to treat sovereign public investment as the moral and financial equivalent of private indebtedness.[3]

References

Coronavirus Aid, Relief, and Economic Security (CARES) Act, Pub. L. No. 116-136, 134 Stat. 281 (2020).
Crawford, N. C. (2021). The costs of war: A 20-year retrospective of the post-9/11 wars. Watson Institute for International and Public Affairs, Brown University.
Economic Growth and Tax Relief Reconciliation Act of 2001, Pub. L. No. 107-16, 115 Stat. 38 (2001).
Economic Recovery Tax Act of 1981, Pub. L. No. 97-34, 95 Stat. 172 (1981).
Federal Reserve Act of 1913, Pub. L. No. 63-43, 38 Stat. 251 (1913).
Inflation Reduction Act of 2022, Pub. L. No. 117-169, 136 Stat. 1818 (2022).
Kelton, S. (2020). The deficit myth: Modern monetary theory and the birth of the people’s economy. PublicAffairs.
Medicare Prescription Drug, Improvement, and Modernization Act of 2003, Pub. L. No. 108-173, 117 Stat. 2066 (2003).
Mosler, W. (1994). Soft-currency economics: A paper on the operating mechanics of the United States monetary system. Adams, Viner and Mosler.
Omnibus Budget Reconciliation Act of 1993, Pub. L. No. 103-66, 107 Stat. 312 (1993).
Second Liberty Bond Act of 1917, Pub. L. No. 65-43, 40 Stat. 288 (1917).
Tax Reform Act of 1986, Pub. L. No. 99-514, 100 Stat. 2085 (1986).
Treasury-Federal Reserve Accord of 1951, 37 Fed. Res. Bull. 267 (1951).
U.S. Const. art. I, § 8, cl. 5.
31 U.S.C. §§ 3101–3121 (Money and Finance: Public Debt).

Notes

[1] Did the government surpluses during the 1990s lead to the dot.com and telecom crashes that followed immediately after?
[2] When I began teaching economics at NYU in 2002, I watched this reversal carefully.
[3] This is a revision of my previous post. See Pennings, A.J. (2024, Sept 29) US Legislative and Regulatory Restrictions on Deficit Spending – Reflecting on Modern Monetary Theory (MMT). apennings.com https://apennings.com/how-it-came-to-rule-the-world/digital-monetarism/us-legislative-and-regulatory-restrictions-on-modern-monetary-theory-mmt/

Category: enabling frameworks, financial news, financial technology, public administration, sustainable development
Tags: Modern Monetary Theory (MMT) > Stephanie Kelton > Warren Mosler

Combining AI with Technological Systems Management (TSM) to Create Integrated BS, MS, and PhD Degrees

Posted on | September 3, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Sep 04) Combining AI with Technological Systems Management (TSM) to Create Integrated BS, MS, and PhD Degrees. apennings.com https://apennings.com/science-and-technology-studies/combining-ai-with-technological-systems-management-tsm-to-create-integrated-bs-ms-and-phd-degrees/

Introduction

In a previous post, I examined Science, Technology, and Society (STS) programs in major universities and how they were beginning to address developments in Artificial Intelligence (AI). This post examines how STS can be combined with Technological Systems Management (TSM) and sustainable development strategies to form a powerful university-level framework for investigating and preparing for an AI-intensive future.[1]

The STS-informed AI strategies emerging at leading institutions such as MIT’s Program in Science, Technology, and Society, Stanford’s STS Program and Human-Centered AI ecosystem, Harvard’s STS work at the Kennedy School, Berkeley’s critical and risk-oriented centers, Cornell’s Global AI Initiative, and Stony Brook’s new Department of Technology, AI and Society within the College of Engineering and Applied Sciences (CEAS) supply critical intellectual and research tools for understanding artificial intelligence as a sociotechnical system.

These strategies emphasize social construction, actor-networks, co-production of knowledge and social order, responsible innovation, power dynamics, equity, and contextual implementation. On their own, however, they risk remaining primarily analytical or critical. When deliberately combined with Technological Systems Management (TSM), the engineering-school tradition exemplified at Stony Brook CEAS that integrates systems thinking, technical competence, and management practice, the result is a powerful educational model. This combination can and should underwrite new or redesigned BS, MS, and PhD degrees that equip graduates with both deep intellectual understanding and practical capabilities in management, innovation, policy, and technical skills.

To maximize societal relevance and global impact, these degrees should be organized organized around a clear ICT4D (Information and Communication Technologies for Development) and AI for Good (AI4Good)/AI4D focus.

The Complementary Strengths

STS AI strategies excel at showing how social groups, institutions, values, and power relations shape AI systems; how agency and accountability are distributed across human and non-human actors; and how research agendas, technical designs, and societal outcomes co-produce one another. They train students to question hype, surface hidden assumptions, anticipate unintended consequences, and design more inclusive and legitimate approaches.

The video below interviews Brian Rosenberg, the former President of the prestigious Macalester College and Visiting Professor at the Harvard Graduate School of Education. He is also the Director and Senior Advisor at African Leadership University (ALU) and the author of “Whatever It Is, I’m Against It: Resistance to Change in Higher Education”.

Technological Systems Management contributes the complementary engineering, leadership, and managerial toolkits. These would include systems modeling and optimization, lifecycle analysis, project and program management, risk and reliability engineering, innovation processes, strategic decision-making, organizational change, value creation, and the ability to move from concept to deployed, maintained, and governed technological systems.

TSM programs have long drawn on the STS premise that complex technologies are “much too important to be left to the engineers alone,” precisely the insight that makes STS-TSM collaboration natural. Together they produce graduates who can:

– Analyze AI sociotechnically (STS intellectual core),
Design, integrate, and manage AI-enabled and enhanced systems at scale (TSM technical and systems core),
– Lead innovation processes and organizational adoption (management and innovation skills),
– Shape and navigate policy and regulatory environments (policy skills),
– And ground all of the above in rigorous technical and project planning understanding.

This is not simply adding “ethics modules” to engineering degrees or “tech literacy” to social-science degrees. It is a fused curriculum in which intellectual depth and practical competence reinforce each other at every level.

ICT4D (Information and Communication Technologies for Development) and AI for Good (AI4Good) Emphasis Strengthens the Model

Standard AI education often prioritizes performance benchmarks, commercial applications, or high-resource environments. An ICT4D / AI4Good/AI4D lens shifts the center of gravity toward solving problems that matter to underserved and low-resource communities, designing systems that function under constraints of infrastructure, data, skills, and governance, addressing global inequalities in access to AI benefits and exposure to AI risks, and ensuring that AI contributes to (rather than undermines) long-term social, economic, and environmental sustainability.

STS frameworks are particularly well-suited to this agenda. They surface issues of power, inclusion, local knowledge, data justice, and the social construction of “development” and “good.” Technological Systems Management supplies the complementary capacity to design, implement, scale, manage, and sustain complex technological systems in real organizational and institutional settings. Together, they produce graduates who can move pilot projects and “AI for Good” narratives into durable, context-appropriate, and accountable AI systems.

This orientation would embed a robust international perspective and a sustained commitment to sustainable development, aligning technical and managerial excellence with the challenges of equity, inclusion, resilience, and the UN Sustainable Development Goals (SDGs). Sustainable development applies to all nations in the age of climate concerns and involves explicitly mapping AI systems to SDG targets for adaptation, resilience, and mitigation applications.

Management, innovation, and policy skills remain central, including strategy and innovation management for development contexts, results-based management, risk and resilience, stakeholder engagement across cultures, and navigation of international policy and funding landscapes. Experiential learning is an important option to the traditional classroom with internshipes, international field projects, collaborations with organizations working in the Global South, and studios that treat social and environmental performance as core design requirements. SUNY Korea, for example, has worked closely with the Asia-Pacific Red Cross / Asia Pacific Disaster Resilience Centre (APDRC) and UN Office of Disaster Risk Reduction located in Songdo, South Korea.

Degree-Level Design

Bachelor of Science

An undergraduate TSM/STS-AI degree would give students foundational technical skills (programming, data systems, basic machine learning, network administration, systems engineering) alongside core STS frameworks applied to AI, introductory management and innovation courses, and policy literacy.

Capstone projects would require students to design or evaluate an AI system while producing both technical deliverables and sociotechnical analyses of stakeholders, risks, values, and implementation conditions. Graduates would be prepared for entry-level roles in AI product teams, systems analysis, responsible innovation units, or policy support positions, with the intellectual agility to grow into more strategic roles.

Master of Science

The MS level is ideal for deeper integration. Students would pursue advanced technical electives or concentrations (e.g., AI systems architecture, data infrastructure, human-AI interaction) while mastering STS analytic methods, technology strategy, innovation management, risk and resilience frameworks, and policy analysis. Applied projects, internships, or client-based work would demand the simultaneous use of technical, managerial, and critical skills. This degree would serve mid-career professionals and strong bachelor’s graduates seeking to lead AI initiatives in industry, government, NGOs, or international organizations—precisely the hybrid talent currently in short supply.

Doctor of Philosophy

A PhD would train researchers and future faculty who can advance both knowledge and practice. Dissertations would typically combine technical or systems contributions with STS-informed analysis of design processes, organizational embedding, policy implications, or societal impacts. Graduates would be positioned to conduct rigorous interdisciplinary research, shape AI research agendas, advise on strategy and policy, and educate the next generation.
Stony Brook’s existing PhD pathway in Technology, Policy, and Innovation, currently expanding toward Technology, AI and Society, offers a natural institutional home for such a degree.

Why This Combination Is Strategically Necessary

Purely technical AI degrees are producing graduates skilled at building models but underprepared for organizational realities, policy constraints, equity considerations, or long-term societal consequences. Purely STS or policy degrees can produce sophisticated critics who lack the technical fluency or systems-management skills needed to intervene effectively in design and deployment. The hybrid model closes this gap.

It aligns with the directions already visible at the institutions discussed earlier. Stanford and MIT demonstrate the value of pairing deep technical environments with STS insight. Harvard shows the power of linking STS to strategy and policy. Berkeley and Cornell illustrate global, justice-oriented, and risk-aware research strategies. Stony Brook’s engineering-school TSM tradition and its current expansion into Technology, AI and Society provide a concrete platform to fuse systems management, technical training, and STS at scale.

Employers, public agencies, and research funders increasingly seek professionals who can operate across these domains. Degrees that deliberately cultivate that combination will produce graduates who can formulate and execute AI strategies that are technically sound, managerially effective, politically informed, and socially robust.

This focused combination addresses a clear talent gap. International organizations, development agencies, governments in the Global South, NGOs, social enterprises, and responsible technology firms need professionals who possess technical fluency, systems-management competence, policy literacy, and the critical intellectual tools to avoid techno-solutionism and neo-colonial patterns in AI deployment. It also aligns with the distinctive strengths of the institutions already advancing STS AI strategies. Cornell’s Global AI Initiative explicitly prioritizes pluralistic and globally relevant AI. Berkeley’s justice-oriented work and Stony Brook’s engineering-school TSM platform (with its expanding AI-and-society mandate) provide natural institutional homes. MIT, Stanford, and Harvard contribute models of rigorous interdisciplinary research and training that can be adapted toward development and sustainability outcomes.

Conclusion

The decisive intellectual and practical contribution comes from uniting traditional STS analysis with Technological Systems Management and in the study of AI. This combination yields degrees that cultivate genuine understanding together with management, innovation, policy, and technical capabilities. International ICT4D and AI4D perspectives, along with sustained attention to AI’s entanglement with global USD structures and fintech systems, serve as valuable supporting emphases.[2] They broaden the empirical scope, raise the stakes of the analysis, and ensure relevance to pressing global challenges without displacing the foundational STS–TSM core. Programs designed on these principles would produce graduates capable of analyzing, designing, managing, and governing AI and related ICT systems with both critical depth and operational competence, whether those systems operate in commercial, public, developmental, or global-financial contexts. That integrated capacity is what the complexity of contemporary AI demands.

Summary

Combining Science and Technology Studies (STS) strategies with Technological Systems Management (TSM) creates an educational model that unites intellectual depth with practical capability. STS supplies the indispensable critical foundation and enabling framework for understanding artificial intelligence in its full social, ethical, and political complexity, while TSM provides the systems discipline, project planning, technical competence, and management leaderships required to act effectively on that understanding. Integrating these domains into coherent BS, MS, and PhD degrees is not merely desirable but represents a logical and necessary transition for contemporary higher education.

Anchoring these programs in an ICT4D and AI4Good / AI4D mission, with a strong international outlook and explicit commitment to sustainable development, makes the model more distinctive and more urgently needed. Attention to AI’s entanglement with global financial structures, fintech systems, and development contexts broadens the empirical scope and ensures relevance to pressing global challenges without displacing the foundational STS–TSM core.

Such programs would prepare a new generation of professionals and scholars capable of analyzing, designing, managing, innovating, and governing AI systems with both critical depth and operational competence. In an era when AI reshapes every sector, this integrated capability—uniting technical effectiveness, equitable design, and globally responsible governance—is precisely what universities, industry, and society require.

Notes

[1] Our undergraduate TSM degree at SUNY Korea has a specialization option in ICT4D. It was started as ICT for Sustainable Development in recognition of the efforts by former UN Secretary General Ban-ki Moon but was changed to the more academically and internationally recognized ICT4D a few years ago.
[2] Rolf Lidskog, Göran Sundqvist; When Does Science Matter? International Relations Meets Science and Technology Studies. Global Environmental Politics 2015; 15 (1): 1–20. doi: https://doi.org/10.1162/GLEP_a_00269
AI Prompt(s) Argue that STS AI strategies should combine with Technological Systems Management to create BS, MS, and PhD degrees that combine intellectual understanding with management, innovation, policy, and technical skills

Category: artificial intelligence, science and technology studies, technology management
Tags: AI4Good > and Society Studies (STS) > ICT4D > Science > Science - Technology - and Society Studies (STS) > Technology > TSM

Emerging AI Frameworks in Leading Science, Technology, and Society (STS) Schools and Centers

Posted on | September 2, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Sep 02) Emerging AI Frameworks in Leading Science, Technology, and Society (STS) Schools and Centers apennings.com https://apennings.com/science-and-technology-studies/emerging-ai-frameworks-in-leading-science-technology-and-society-sts-schools-and-centers/

Introduction

Science, Technology, and Society (STS) frameworks remain critical for AI analysis and governance because they treat artificial intelligence as a sociotechnical system shaped by, and in turn reshaping, institutions, power, social relations, values, and material infrastructures, rather than as a purely technical object to be controlled through standards or rules alone.

Drawing on classic STS approaches and the emerging strategies visible in major STS programs, including those highlighted by me previously and active initiatives at MIT, Stanford, Harvard, Berkeley, Cornell, Stony Brook, and related centers), this overview maps the most relevant frameworks and shows how leading schools are operationalizing them for AI.[1]

Emerging AI Strategies in Leading STS Schools and Centers

The major US STS nodes such as MIT, Stanford, Berkeley, Harvard, Cornell, and Stony Brook (Department of Technology and Society in CEAS) are places where STS has long intersected with engineering, policy, and management. Recent developments show these programs actively shaping AI governance agendas:

MIT Program in Science, Technology, and Society continues its foundational role while engaging AI through ethics, journalism, and Institute-wide reflection on education and research norms. Faculty and events address governance, liability, and the societal embedding of AI, often in dialogue with the Schwarzman College of Computing.

Stanford Univeristy STS Program and HAI offers concentrations and courses that deconstruct AI hype, examine values built into systems, and link technical and social analysis. The broader Stanford ecosystem (HAI, Law AI Initiative, RegLab) operationalizes STS insights into human-centered AI, policy research, and governance tools, emphasizing interdisciplinary collaboration between STS, computer science, law, and management science.

Harvard Program on Science, Technology, and Society (Kennedy School) focuses on policy and institutional design. Recent work advances proactive, power-sharing approaches to AI governance that prioritize human flourishing, democratic stability, and economic empowerment over purely reactive risk management.

UC Berkeley’s CSTMS and AI Security Initiative at CLTC combines critical theory and social-justice orientations with practical risk-management standards development. Emphasizes multistakeholder processes, equity in benefit distribution, and contributions to national and international standards (including NIST-related efforts).

Cornell University’s Department of Science and Technology Studies + Global AI Initiative integrates global and pluralistic perspectives into AI research, design, evaluation, and governance. Focus areas include inclusive AI for diverse communities, transparency, accountability, and public oversight, with explicit attention to translating research into policy and practice.

Stony Brook University’s Department of Technology and Society has a new Department of Technology, AI and Society in the College of Engineering and Applied Sciences (CEAS). It is building on its long-standing TSM tradition and the motto that engineering is “much too important to be left to the engineers,” Stony Brook’s Department of Technology and Society is expanding into a dedicated Technology, AI and Society department with a new chair. NY State investment supports faculty hiring, new degree pathways, and research organized around ethics, equity, and justice, with applications to energy, health, and societal challenges. This represents a direct engineering-school pathway for STS-informed AI systems management and governance.

Other programs (Edinburgh, Twente, UC San Diego, etc.) similarly emphasize multidisciplinary training and critical engagement, reinforcing a global STS conversation on AI.

Implications for Governance

These frameworks and institutional strategies converge on several practical orientations. Governance must be process-oriented, participatory, and reflexive rather than purely compliance-based. Accountability should be traced across networks rather than assigned to a single locus. Technical knowledge claims and social ordering must be examined together. Context, power, and global diversity matter; one-size-fits-all technical standards are insufficient.

Engineering and policy education should integrate STS tools so that future designers and managers treat social acceptance, legitimacy, equity, and sustainability as core system requirements.

Leading STS programs are already institutionalizing these insights through new courses, concentrations, research initiatives, policy engagement, and (in cases such as Stony Brook) departmental reorganization around AI and society. The result is a maturing field in which classic STS analytics such as SCOT, ANT, co-production, sociotechnical systems, and RRI—are being refined and applied to the distinctive challenges of general-purpose, generative, and agentic AI.

Some Major STS Frameworks from Major Universities Applied to AI Governance

Social Construction of Technology (SCOT)

Different social groups interpret AI differently until “closure” stabilizes dominant meanings of safety, fairness, or intelligence. Governance processes themselves become sites of negotiation among developers, regulators, affected communities, labor, and Global South actors. Stanford’s STS courses (e.g., “Constructing and Deconstructing Artificial ‘Intelligence’”) explicitly train students to question hype and examine whose values are inscribed in systems.

Actor-Network Theory (ANT)

AI systems emerge from heterogeneous networks of human and non-human actants (algorithms, datasets, chips, standards, legal texts, users). Agency and accountability are distributed and can be displaced. Recent applications map how responsibility shifts in generative and agentic AI, medical AI, and public-sector deployments. This lens is especially useful for tracing why accountability often fails to land on the most powerful actors.

Co-production (Jasanoff and others)

Ways of knowing AI (benchmarks, risk assessments, capability evaluations) and ways of ordering society (who is protected, who decides, what counts as harm) are produced together. Governance frameworks do not merely regulate a pre-existing technology; they help constitute what legitimate AI is. Harvard’s Program on Science, Technology, and Society (housed at the Kennedy School) and related work on power-sharing liberalism exemplify this orientation, linking knowledge production to democratic and institutional design.

Sociotechnical Systems Thinking and Its Extensions

Classic joint optimization of social and technical subsystems has been updated for intelligent and agentic systems. Newer “intelligent sociotechnical systems” approaches examine how AI agents can themselves reconfigure coordination structures, creating recursive governance challenges. This resonates with engineering-school STS traditions that emphasize systems management.

Responsible Research and Innovation (RRI)

Anticipation, inclusion, reflexivity, and responsiveness provide a practical STS-informed governance orientation. Leading programs adapt these dimensions to AI through upstream engagement, continuous reflection, and adaptive institutions.

Complementary Lenses

Infrastructure studies reveal the invisible classification systems and data infrastructures that quietly govern outcomes. Feminist and standpoint STS highlight situated knowledges and care. Critical approaches question technological determinism and open possibilities for democratizing design and oversight. Environmental and justice-oriented STS (strong at Berkeley’s CSTMS) foreground energy, material, and equity impacts.

In short, STS does not merely critique AI governance; it supplies the conceptual and institutional resources for building more legitimate, adaptive, and equitable governance arrangements. The programs at MIT, Stanford, Harvard, Berkeley, Cornell, Stony Brook, and peer institutions demonstrate how these frameworks are moving from theory into curriculum, research agendas, and real-world policy influence.

Implications for AI Strategies, Research, and Technical Training

STS frameworks encourage AI strategies that are:

Research-informed and reflexive — Research agendas incorporate anticipation of social effects, diverse knowledge inputs, and ongoing evaluation of assumptions.

Technically rigorous yet context-aware — Technical training (programming, systems design, evaluation methods, machine learning fundamentals) is paired with skills in stakeholder analysis, ethical reasoning, implementation studies, and impact assessment.

Network and systems-oriented — Strategies address the full assemblage of data, models, infrastructure, organizations, and users rather than isolated technical components.
Inclusive and power-sensitive — Training and research design deliberately surface whose knowledge counts and who benefits.
Adaptive — Both research programs and educational curricula build capacity for continuous learning as AI capabilities evolve.

In practice, this produces AI strategies that integrate technical roadmaps with organizational change plans, research priorities with public value considerations, and skill development with critical literacy. Graduates and researchers trained in these environments are equipped to design, evaluate, and steer AI systems as sociotechnical endeavors.

Leading STS programs at MIT, Stanford, Harvard, Berkeley, Cornell, Stony Brook, and peer institutions demonstrate how classic frameworks such as SCOT, ANT, co-production, sociotechnical systems thinking, and RRI and how they are being translated into concrete research agendas, technical training pathways, and institutional AI strategies. The result is a maturing approach in which technical excellence and sociotechnical insight are pursued together as essential components of effective AI strategy.

Notes

[1] At the State University of New York, Korea, we offer the Stony Brook curriculum from the Department of Technology, AI, and Society in New York.
AI Prompt(s) Review AI strategies emerging in STS schools in MIT, Cornell, Stony Brook, Harvard, Stanford, Berkely and other schools mentioned in my previous research on the increasing importance of STS.

Category: artificial intelligence, science and technology studies, sustainable development

Symphony of Science – The Quantum World and the Four Forces of Nature

Posted on | September 1, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Sep 01) Symphony of Science – The Quantum World and the Four Forces of Nature. apennings.com https://apennings.com/science-and-technology-studies/symphony-of-science-the-quantum-world-and-the-four-forces-of-nature/

Introduction

My EST 202 – Introduction to Science and Technology course is known for starting off with this song about the “four forces of nature.” Featuring Morgan Freeman, Stephen Hawking, Michio Kaku, Brian Cox, Richard Feynman, and Frank Close, “The Quantum World” is one of the Symphony of Science music video collections. With over 12 million views, it provides quick intro to the nature of atoms and subatomic particles, the “jiggly things that make up everything we see.” It features Morgan Freeman, Stephen Hawking, Michio Kaku, Brian Cox, Richard Feynman, and Frank Close.

The song is a three-and-a-half-minute auto-tuned chorus of physicists, mostly from John Boswell’s Symphony of Science video The Quantum World, the eleventh in the series. Morgan Freeman’s opening is the STS hook: dig inside the atom and you find tiny particles held together by invisible forces. Cox then delivers the line the class keeps: the universe is made of twelve particles of matter and four forces of nature.

That is “a wonderful and significant story.” It is also a good place to begin a course on science, technology, and society. Before we get to labs, laptops, satellites, or AI, we start with the claim that everything visible is assembled from a short list of ingredients and four interactions.

Feynman is the classroom favorite: the world is a dynamic mess of jiggling things; little things behave very differently from anything big. Michio Kaku adds that even Einstein never quite made peace with quantum theory. Stephan Hawking wants a theory of everything that is still just beyond our grasp. For STS, that unfinished sentence matters. Science is a social practice of models that work—and of gaps the models cannot yet close.

The video itself notes a further gap: dark matter and dark energy are thought to make up most of the universe, in addition to the twelve particles and four forces. So the “complete” inventory is already incomplete. That, too, is an STS lesson.

Cox’s “four forces of nature” are the four fundamental interactions of the Standard Model plus gravity. Three are well described by quantum field theory.

Gravity is the holdout.

Gravity is the weakest of the four at the scale of atoms, and the one that dominates at the scale of planets. It pulls mass toward mass. It keeps you on the floor, the Moon in orbit, and galaxies from flying apart. Drop a phone and gravity wins. Launch a satellite and engineers spend careers negotiating with it. Unlike the other three, gravity still lacks a fully working quantum theory; the hypothesized carrier particle, the graviton, has not been observed. Hawking’s “theory of everything” is, in large part, the unfinished marriage of gravity and quantum mechanics.

Electromagnetism is the force of everyday technology. Opposite charges attract; like charges repel. Photons carry the interaction. It holds electrons around nuclei, which is why atoms have structure and chemistry exists. It is also light, radio, Wi-Fi, magnets, electric motors, and the reason your laptop does not fall through the desk: electromagnetic repulsion between electron clouds. If gravity writes the large-scale architecture of the cosmos, electromagnetism writes the user’s manual for circuits, screens, and almost every device in an STS classroom.

The strong nuclear force is the short-range glue inside the nucleus. Gluons bind quarks into protons and neutrons, and hold those protons and neutrons together despite their electromagnetic urge to fly apart. Without it there are no atomic nuclei, no periodic table, no stars fusing hydrogen into helium. A nuclear reactor and a supernova are both, in different registers, public performances of the strong force. Its range is tiny—roughly the size of a nucleus—which is why you do not feel it when you pick up a book, even though it is far stronger than gravity or electromagnetism at that distance.

The weak nuclear force is the specialist in transformation. Carried by W and Z bosons, it changes one type of particle into another. That is how a neutron can become a proton, an electron, and an antineutrino—beta decay. The Sun shines because the weak force lets protons in the core convert into neutrons as hydrogen fuses into helium. Carbon-14 dating works because the same interaction slowly changes radioactive carbon in old bone and wood. The weak force is why elements transmute and why stellar fusion is not instantaneous.
Together the four forces do the work Freeman names at the start: they hold the jiggling world in place, or let it change. Gravity gathers; electromagnetism structures and signals; the strong force binds the nucleus; the weak force permits the alchemy that lights stars and dates fossils.

Why start an STS course here?

The Quantum World is not a substitute for a physics textbook. It is a compact cultural object: science edited into pop, authority figures auto-tuned, a Standard Model chorus with a footnote about dark matter. Students meet atoms as “packets of energy born in cosmic furnaces,” then spend the semester asking how those same atoms become instruments, infrastructures, and political facts—semiconductors, power grids, satellites, medical isotopes, nuclear policy.
Feynman signs off by leaving us something to imagine. That is the right last line for week one. The four forces are not only physics. They are the conditions under which every later technology in the course is even possible.

Symphony of Science – the Quantum World!

Lyrics

[Morgan Freeman]
So, what are we really made of?
Dig deep inside the atom
and you’ll find tiny particles
Held together by invisible forces

Everything is made up
Of tiny packets of energy
Born in cosmic furnaces

[Frank Close]
The atoms that we’re made of have
Negatively charged electrons
Whirling around a big bulky nucleus

[Michio Kaku]
The Quantum Theory
Offers a very different explanation
Of our world

[Brian Cox]
The universe is made of
Twelve particles of matter
Four forces of nature

That’s a wonderful and significant story

[Richard Feynman]
Suppose that little things
Behaved very differently
Than anything big

Nothing’s really as it seems
It’s so wonderfully different
Than anything big

The world is a dynamic mess
Of jiggling things
It’s hard to believe

[Kaku]
The quantum theory
Is so strange and bizarre
Even Einstein couldn’t get his head around it

[Cox]
In the quantum world
The world of particles
Nothing is certain
It’s a world of probabilities

(refrain)

[Feynman]
It’s very hard to imagine
All the crazy things
That things really are like

Electrons act like waves
No they don’t exactly
They act like particles
No they don’t exactly

[Stephen Hawking]
We need a theory of everything
Which is still just beyond our grasp
We need a theory of everything, perhaps
The ultimate triumph of science

(refrain)

[Feynman]
I gotta stop somewhere
I’ll leave you something to imagine

“The Quantum World” is the eleventh installment in the ongoing Symphony of Science music video series. Materials used in the creation of this video are from:

http://symphonyofscience.com for downloads & more videos!

Richard Feynman – Fun to Imagine
BBC Visions of the Future – the Quantum Revolution
Through the Wormhole with Morgan Freeman
Into the Universe with Stephen Hawking
Brian Cox TED Talk
BBC What Time is it
BBC Wonders of the Universe
BBC Horizon – What Is Reality

Category: science and technology studies

A Digital Bobsled in ICT4D

Posted on | August 29, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Aug 29) A Digital Bobsled in ICT4D. apennings.com https://apennings.com/digital-geography/a-digital-bobsled-in-ict4d/

Introduction

Cool Runnings (1992) (“peaceful journey”) is not simply as an underdog sports story, but as a parable about entering a technological world without having to become culturally or economically identical to the countries that built it.[1]

My earlier post makes this case for appropriate development. Jamaica enters a highly technical global arena with limited resources. Still, the team succeeds by combining access to the global system with its own capabilities—especially speed, teamwork, improvisation, and determination.

Cool Runnings and the Digital World: The Jamaican Bobsled Team as Digital Nomads

Cool Runnings can be read as a surprisingly good metaphor for development in the digital age. Jamaica has no snow, no established bobsled tradition, and almost none of the infrastructure associated with the Winter Olympics. Yet four Jamaican athletes decide to enter the competition anyway.

The premise seems absurd. What could Jamaica possibly have to contribute to a sport developed in the cold-weather industrial societies of Europe and North America? That is precisely what makes the story interesting.

The Jamaican bobsled team can be understood as an early metaphor for the digital nomad. Someone who enters a global technological environment without necessarily possessing the geographic, institutional, or infrastructural conditions from which that environment originally emerged.

The digital world, like the Olympic bobsled track, has rules, standards, protocols, platforms, and infrastructure largely developed elsewhere. The Internet, cloud computing, digital payments, artificial intelligence, satellite networks, and global software platforms were overwhelmingly designed and financed in advanced industrial economies. Yet their use is no longer restricted to those places.

A smartphone with a good broadband connection can put a person in Nairobi, Kingston, Dhaka, Manila, or Lima into the same basic digital environment as someone in New York, London, Seoul, or San Francisco.

The important question therefore emerges. It is no longer simply whether developing countries can reproduce the technological infrastructure of the rich countries. It becomes, what can people do when they gain access to global technological infrastructure while bringing their own skills, cultures, institutions, and resourcefulness with them? That is the deeper meaning of the Jamaican bobsled metaphor.

You Don’t Have to Build the Snow

The Jamaicans cannot manufacture Calgary’s climate. They cannot reproduce decades of European bobsled training. They cannot suddenly acquire the financial resources, equipment, coaching networks, and institutional experience of the established teams.

But they don’t necessarily need to. They need access to the track. They need a sled. They need enough knowledge to understand the rules. And they need to figure out what they can do better.

The film dramatizes this through the team’s adaptation of its sprinting ability to the explosive push-start. The Jamaican athletes bring something from their existing environment that becomes useful in an unfamiliar technological and institutional environment. This is the point of appropriate development.

Development does not necessarily mean reproducing the path followed by the first countries to develop. It means combining globally available technologies with locally available capabilities. That distinction matters especially in the digital world. A country does not necessarily need to build its own Bloomberg Terminal, cloud computing ecosystem, global satellite network, or semiconductor industry before its citizens can participate in digital markets and spaces. It can enter the network. Once inside, local capabilities become productive in ways that were previously impossible.

The Digital Nomad Has a Jamaican Bobsled

The digital nomad is therefore an interesting figure because they carry relatively little physical infrastructure. A laptop replaces an office. A smartphone app replaces a bank branch. Cloud computing replaces much of the local computing infrastructure. Digital platforms enhance conventional distribution networks.

Blockchain wallets can replace some traditional financial intermediaries. Video conferencing replaces some physical travel. Artificial intelligence increasingly replaces portions of specialized knowledge that previously required proximity to major institutions.

Individuals become mobile because the infrastructure has become distributed. This doesn’t mean that geography disappears. Quite the opposite. Reliable electricity, broadband, education, transportation, financial institutions, and political stability remain enormously important.

But the threshold for participating in the global economy has fallen dramatically. That is the Jamaican bobsled analogy’s great significance. The digital economy reduces the physical infrastructure an individual needs to participate in sophisticated economic activity.

The Jamaican team does not bring Jamaica’s entire winter-sports infrastructure to Calgary. It brings four people.
And those four people bring what they know how to do.

From Appropriate Technology to Appropriate Digital Technology

This also extends the idea of appropriate technology developed in my original essay. Appropriate technology is sometimes misunderstood as meaning “low technology.” That is not the point. Appropriate technology means technology fitted to circumstances.

A solar microgrid can be more appropriate than a centralized power plant in a remote community. Mobile money can be more appropriate than building thousands of bank branches. Satellite connectivity can be more appropriate than waiting decades for terrestrial broadband infrastructure. And increasingly, AI can be appropriate when it augments local expertise rather than attempting to replace it.

This suggests a different philosophy of ICT4D. The goal should not be to turn every developing country into a smaller version of Silicon Valley. The goal should not be to turn every developing country into a smaller version of Silicon Valley. The goal should be to provide access to global digital infrastructure while allowing local communities to decide what to do with it. The Jamaican bobsled team does not become Swiss. It becomes Jamaican bobsled. That distinction is the whole point.

Global Infrastructure, Local Agency

This metaphor also has a political-economic dimension. Global infrastructure creates possibilities, but infrastructure alone does not produce development. A fiber-optic cable does not create a business. A digital wallet does not create income. An AI system does not automatically create productive capacity.

A blockchain does not automatically produce social welfare. The infrastructure must meet human agency. This is why the Jamaican team is more interesting than a simple story about technology transfer. The technology, or in this case, the sporting infrastructure, is only the enabling environment.

The real developmental resource is agency. The team takes a system designed by others and finds a way to participate on its own terms. This is precisely the challenge facing developing countries in the digital economy.

They should not be treated merely as markets for American, European, Chinese, or other foreign technologies. They should become active participants in designing applications, businesses, institutions, and development strategies appropriate to their own circumstances.

The Global Digital Track

The metaphor can be extended even further. The Internet is the track. Standards are the rules. Cloud computing is part of the infrastructure. Digital wallets are the vehicles.
Blockchain provides new forms of synchronized accounting. AI increasingly provides navigation, prediction, translation, optimization, and coordination. And human beings remain the athletes.

This is why digital development can be so powerful for countries that historically lacked the capital required to reproduce the infrastructure of industrial economies.

The digital environment allows a degree of leapfrogging. A country doesn’t necessarily have to pass through every institutional stage experienced by the United States or Western Europe. Mobile money can leap over branch banking. Digital platforms can leap over conventional distribution systems. Renewable microgrids can leap over centralized fossil-fuel infrastructure. Digital education can supplement physical universities. Telemedicine can extend specialist knowledge beyond major hospitals. AI can give a small business access to analytical capabilities previously available only to large corporations. These are not guarantees of development. But they alter the possibilities.

And Then Comes the Sunny Day

This brings us back to Jimmy Cliff. “I Can See Clearly Now” is an unusually appropriate soundtrack for this argument because its central metaphor is not simply victory. It is visibility. The rain disappears. The obstacles become visible. And once they become visible, they can be navigated.

That is also what digital technology can provide: not development automatically, but greater visibility into the possibilities for action. A farmer can see prices. A worker can see international employment opportunities. A small manufacturer can see global customers. A student can see educational resources. A migrant can see a way to send money home. A community can see weather and climate information.

A government can see infrastructure conditions through satellite imagery. An entrepreneur can see a market beyond the boundaries of a small domestic economy. The digital world does not eliminate obstacles. It can make them more legible and actionable.

And that distinction connects directly to my broader work on media and development. A medium becomes economically significant when it does more than represent the world—when it changes what actors can see, calculate, coordinate, and do. That is the movement from representation toward operative mediation.

The Bright, Bright, Bright Sunny Day

The ending of Cool Runnings therefore works beautifully as a metaphor for digital development. The Jamaican team does not win the gold medal. Historically, the Jamaican four-person team finished 26th in Calgary after completing three runs; the film transforms the episode into a more dramatic story of dignity and perseverance.

But that is exactly why the story works. The point is not that Jamaica suddenly became a winter-sports superpower. The point is that Jamaica entered the system. It showed that participation was possible. It brought its own resources into an environment not designed around them. And it left the track with something more important than a medal. It left with evidence that the boundaries of participation were not as fixed as they had appeared.

That is the promise of the digital world for developing countries. The objective should not be to make everyone look like Silicon Valley. It should be to make the global digital infrastructure sufficiently open, affordable, interoperable, and accessible that people everywhere can bring their own capabilities into it.

The Jamaican bobsledders didn’t need Jamaica to become Calgary. They needed a way onto the track. And perhaps that is the most optimistic way to think about digital development. Give people access to the track. Let them bring their own sled. Let them figure out how to run it. Then, perhaps, the clouds begin to lift.

And as Jimmy Cliff sings, the future becomes visible: a bright, bright, bright sunshiny day.

Notes

[1] I am using Cool Runnings and Jimmy Cliff’s “I can see clearly now” to support my EST 230 – ICT for Sustainable Development class. A central course in the ICT4D specialization, in our BS in Technological Systems Management.

Category: artificial intelligence, books and films, creative cities, cultural Industries, democratic political economies, digital geography, disaster risk management, science and technology studies, smart new deal, sustainable development, visual literacy and rhetoric
Tags: Appropriate Development > Cool Runnings > ICT4D > Jimmy Cliff

The Computerization of Society Revisited: French Social Theory and the Geopolitics of Information and Data Centers in the Age of AI

Posted on | August 29, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Aug 29) The Computerization of Society Revisited: French Social Theory and the Geopolitics of Information and Data Centers in the Age of AI. apennings.com https://apennings.com/how-it-came-to-rule-the-world/the-computerization-of-society-revisited-french-social-theory-and-the-geopolitics-of-information-and-data-centers-in-the-age-of-ai/

Introduction

The publication of L’Informatisation de la société in 1978, co-authored by Simon Nora and Alain Minc and later published in English as The Computerization of Society by MIT Press, represented a watershed event in the socio-political analysis of technology. Commissioned by President Valéry Giscard d’Estaing, the text transformed computing from an esoteric technical concern into a central battlefield of state sovereignty, political economy, and democratic participation. By coining the term télématique to describe the merger of telecommunications networks and computers, Nora and Minc formulated an early structural critique of digital infrastructure. Their work anticipated the core dilemmas that subsequently defined international development informatics and contemporary artificial intelligence (AI).

In 1983, I started a year-long internship at the East-West Center’s Communication Institute in Honolulu to assist a new project, the Computerization Policy Project with Syed Rahim and Meheroo Jussawalla. Norm Abramson (ALOHANET), Herbert Dordick. and Deane Neubauer were also part of the project, and we worked closely with the Asian Media Information and Communication Centre (AMIC) in Singapore. My office was located next to Wilbur Schramm, the author of Mass Media and National Development (1964) and founder of Communication Studies. Syed Rahim and I published Computerization and Development in Southeast Asia (1987) before I went on for my PhD studies. It was an exciting time of intellectual debate and discovery that solidified my interest in this area.

In this post, I review the impact and roots of Nora-Minc Report, and emergence of an approach to the combination of telecommunications and data processing, which the report calls “telematics.”[1]

Intellectual Roots and the Post-Industrial State

The philosophical underpinnings of the Nora-Minc report reflected four converging intellectual currents in late-1970s France. The primary anchor was the tradition of dirigisme and Gaullist technological sovereignty, which viewed the state not as a passive regulator but as an active guarantor of national autonomy against foreign corporate monopolies. This administrative ethos was complemented by French post-industrial sociology, most notably Alain Touraine’s concept of the programmed society and Daniel Bell’s analysis of “post-industrial” information economies. Nora and Minc adopted the view that power in modern civilizations no longer derived solely from physical capital or raw industrial throughput, but from command over information systems and communication circuits.

Simultaneously, the authors engaged with the anti-bureaucratic critique championed by the French Second Left (Deuxième Gauche) and institutional sociologists like Michel Crozier. Rather than advocating a monolithic, top-down computational apparatus, the report absorbed the ethos of autogestion (self-management). It proposed that decentralized data grids could dismantle bureaucratic gridlock, flatten rigid state hierarchies, and foster local civic engagement. This institutional optimism was tempered by the post-structuralist concerns of Michel Foucault and early critical data theorists. Nora and Minc warned that ungoverned computerized files could easily crystallize into an invasive surveillance apparatus, a fear that directly hastened the establishment of France’s landmark data protection authority, the Commission Nationale de l’Informatique et des Libertés (CNIL).

The Legacy of Jean-Jacques Servan-Schreiber

The analytical lineage of the report owed a profound debt to Jean-Jacques Servan-Schreiber and his seminal 1967 text, The American Challenge. Both Simon Nora and Servan-Schreiber had emerged from the technocratic reform circles surrounding Prime Minister Pierre Mendès France and had collaborated in founding the political weekly L’Express. While Servan-Schreiber originally alerted Europe to American industrial, managerial, and computational supremacy, Nora and Minc modernized this warning for the era of networked telecommunications.

The two visions diverged in their practical remedies. Servan-Schreiber advocated for private corporate amalgamations, liberalized capital, and the internal adoption of American managerial techniques to build transnational European champions. In contrast, Nora and Minc argued that computing had evolved into a public utility requiring direct state intervention. This dialogue reached full maturity in 1980 when Servan-Schreiber published The World Challenge, embracing Nora and Minc’s vocabulary and convincing President François Mitterrand to establish the World Centre for Computer Science and Human Resources in Paris to foster technological transfer toward developing nations.

Confronting the Threat of IBM and Foreign Networks

At the core of the report stood an uncompromising critique of IBM and transnational corporate networks. Nora and Minc argued that the era of mainframe hardware competition was obsolete. The decisive axis of power had shifted to network dominance, transmission protocols, and proprietary data standards. They warned that IBM was rapidly moving to capture the global network layer through initiatives like proprietary System Network Architecture and commercial satellite ventures, positioning itself as an unaccountable supranational utility.

The authors argued that permitting foreign monopolies to manage national data traffic represented a form of knowledge colonization. If a sovereign state stored and processed its public archives, economic records, and corporate data within foreign databases, it would surrender its collective memory and administrative independence. To prevent France from becoming an informational colony, Nora and Minc proposed bypassing closed hardware competition in favor of building an open, publicly governed network infrastructure. This strategic pivot became the ideological and structural catalyst for the nationwide rollout of the public packet-switching network and the Minitel system.

Historical Significance and Contributions to ICT4D

The Nora-Minc report established a critical precedent for the field of Information and Communication Technologies for Development (ICT4D). Prior to 1978, international discourse on development computing was largely confined to technical modernization theory, treating computers as value-neutral tools that automatically produced progress when imported into lower-income economies. Nora and Minc systematically dismantled this technological determinism. They established that information systems are inextricably bound to structural power asymmetries, domestic political institutions, and international trade dependencies.

Their analysis furnished ICT4D scholars and practitioners with an enduring conceptual foundation. The report demonstrated that digital tools inherently reflect the geopolitical and economic interests of their creators, presaging critical development studies on global data extraction and techno-dependency. By rejecting corporate lock-in and prioritizing public utility grids, Nora and Minc anticipated modern debates regarding digital public goods, open standards, and the digital divide. The report also articulated the fundamental ICT4D premise that social transformation depends on legal architectures, institutional capacity, and civic participation rather than raw computational throughput.

Framing the Contemporary Analysis of Artificial Intelligence

The analytical framework developed by Nora and Minc remains applicable to the political economy of artificial intelligence. Modern foundation models, centralized cloud server farms, and frontier generative systems reproduce the structural threats that Nora and Minc identified during the mainframe and early telematics eras.

Applying their framework to contemporary AI illuminates several urgent structural dynamics, including compute hegemony and the sovereign AI stack; data justice and cultural enclosure; and, democratic legitimacy versus algorithmic governance.

Just as Nora and Minc warned against IBM monopolizing the telecommunications layer, the modern state confronts a concentrated oligopoly of transnational cloud providers that control specialized hardware, model weights, and compute clusters. The report’s insistence on infrastructural autonomy directly informs current initiatives to build sovereign compute capabilities, public cloud alternatives, and open-weight foundational models.

The report’s warning regarding the alienation of knowledge anticipates contemporary concerns over data extraction. Large model architectures trained on non-Western cultural or administrative data without consent mirror the informational colonization Nora and Minc critiqued. Their work underscores the necessity of domestic data governance, linguistic representation in training corpuses, and civic control over algorithmic knowledge banks.

Nora and Minc demonstrated that computational optimization cannot substitute for democratic deliberation. In an era where automated decision systems, predictive public-sector algorithms, and corporate AI ethics programs proliferate, their work reminds policymakers that technological adoption must remain strictly subordinate to democratic accountability, institutional transparency, and statutory human rights.

Summary

Commissioned by French President Valéry Giscard d’Estaing in 1978, Simon Nora and Alain Minc’s landmark report L’Informatisation de la société (translated in 1980 by MIT Press as The Computerization of Society) fundamentally altered how modern states analyze computing, telecommunications, and social development. Coining the term télématique, the authors synthesized several major French intellectual currents—Gaullist dirigisme, Touraine and Bell’s post-industrial sociology, Michel Crozier’s institutional critique of bureaucratic paralysis, and early Foucauldian warnings against the surveillance state. The report built directly upon the warning of American technological dominance first sounded in Jean-Jacques Servan-Schreiber’s The American Challenge (1967), but parted ways by insisting that digital networks required public, state-coordinated utility infrastructure rather than purely private corporate consolidation.

A central achievement of the report was its structural critique of IBM and transnational corporate networks. Nora and Minc argued that the battleground of technological sovereignty had shifted from mainframe hardware manufacturing to the control of network layers, communication protocols, and centralized databases. Leaving this infrastructure in the hands of foreign private monopolies, they warned, would result in the “alienation of knowledge” and transform sovereign states into dependent informational colonies. In response, they championed open, decentralized public networks. This was a strategy that preserved national strategic autonomy, spurred the establishment of France’s data protection authority (CNIL), and laid the technical groundwork for the nationwide rollout of the Minitel, Frances pre-Internet telecommunications web.

The enduring legacy of the Nora-Minc report extends directly into the foundation of Information and Communication Technologies for Development (ICT4D) and the contemporary governance of artificial intelligence. By dismantling naive technological determinism, the report established that information systems are inextricably tied to global power asymmetries, economic dependencies, and domestic institutional capacity. Today, as nations confront the oligopoly of frontier AI models, centralized cloud compute clusters, and extractive training datasets, Nora and Minc’s analytical framework provides an indispensable blueprint for theorizing sovereign compute, data justice, and democratic accountability in an increasingly automated world.

Conclusion

The Nora-Minc report represents the foundational moment when computing was first comprehensively analyzed through the dual lenses of sovereign statecraft and critical social theory. By unmasking the geopolitical motives embedded in proprietary communications networks and rejecting technological fatalism, Simon Nora and Alain Minc provided an enduring intellectual template. Their insights laid the groundwork for critical development informatics and continue to provide indispensable conceptual tools for confronting the structural, infrastructural, and democratic challenges posed by artificial intelligence in contemporary society.

References

Bell, D. (1973). The Coming of Post-Industrial Society: A Venture in Social Forecasting. New York: Basic Books.
Crozier, M. (1970). La Société bloquée. Paris: Éditions du Seuil.
Foucault, M. (1975). Surveiller et punir: Naissance de la prison. Paris: Gallimard.
Heeks, R. (2018). Information and Communication Technology for Development (ICT4D). London: Routledge.
Lyotard, J. F. (1979). La Condition postmoderne: rapport sur le savoir. Paris: Éditions de Minuit.
Nora, S. and Minc, A. (1978). L’Informatisation de la société: rapport à M. le Président de la République. Paris: La Documentation Française.
Nora, S. and Minc, A. (1980). The Computerization of Society: A Report to the President of France. Cambridge, MA: MIT Press.
Rahim, S. and Pennings, A.J. (1987) Computerization and Development in Southeast Asia. AMIC.
Schramm, W. (1964) Mass Media and National Development. UNESCO.
Servan-Schreiber, J. J. (1967). Le Défi Américain. Paris: Denoël.
Servan-Schreiber, J. J. (1980). Le Défi Mondial. Paris: Fayard.
Touraine, A. (1969). La Société post-industrielle: Naissance d’une société. Paris: Denoël.

Notes

[1] Télématique combined telecommunications and computers. It was one of the most interesting uses of vocabulary to distinguish different positions related to this emerging technological area. Informatics quickly countered it, emphasizing the computer side, and has had a longer history of usuage. In the US, the computer industry adopted the term “online” because data communications suggested the FCC could regulate it.
[2]
AI Prompt(s) L’Informatisation de la société, 1978 by Simon Nora and Alain Minc, commissioned by French President Valéry Giscard d’Estaing and published by MIT Press in 1980 as The Computerization of Society: A Report to the President of France marked a pivotal shift in how computers and information technologies were analyzed in relation to society and development. Trace the philopsophical roots of the report to the intellectual movements in France at the time. Combine those last three response in an article for publication with relevant citations and references listed at the end. Provide a summary and conclusion on how the Nora-Minc report contributed to the emergence of critical analysis of computers and networks worldwide and its contribution to ICT4D. Suggest how it can contribute to an analysis of AI in society.

CIPS vs. SWIFT: Dedollarization or Global Public Good?

Posted on | August 26, 2026 | No Comments

Citation APA (7th Edition)

Pennings, A.J. (2026, Aug 27) CIPS vs. SWIFT: Dedollarization or Global Public Good? apennings.com https://apennings.com/digital-geography/the-100-trillion-debt-era-mmt-as-permission-sact-as-engine/

Introduction

The competition between China’s Cross-Border Interbank Payment System (CIPS) and the Western financial infrastructure represented by Society for Worldwide Interbank Financial Telecommunication (SWIFT) is often described as a technological contest where one payment network is replacing another. But the deeper issue is who gets to coordinate economic activity around the world.

The contest between CIPS with the Chinese Renminbi on one side and SWIFT messaging system on the other is not a simple competition between two currencies. It is a collision between two fundamentally different financial architectures that reflect divergent visions of the international political economy.

In this post, I explore where global commerce and finance are heading by suggesting we look past headline exchange rates and examine the underlying financial plumbing. It is important to review the systemic philosophies, technical mechanisms, trade patterns, and vulnerabilities that separate a sovereign hub like CIPS from a (mostly) global commons like the Society for Worldwide Interbank Financial Telecommunication (SWIFT), which is heavily oriented towards the USD.[1]

At the level of systemic philosophy, the two networks serve opposite economic models. CIPS and the RMB are designed as a national and sovereign hub whose primary function is to anchor bilateral trading partners directly into the Chinese domestic economy and financial system. It operates as an instrument of strategic sovereignty and industrial coordination, establishing a secure, state-monitored channel that connects counterparties directly to Beijing.

In contrast, SWIFT and the USD financial system function as a flawed but globally enabling commons. Built to facilitate multilateral commerce between third-party nations, the dollar infrastructure operates as universal, open-ended connective tissue that allows two non-US entities to finance and settle trade without touching either domestic banking system. For instance, a trade between a Brazilian exporter and a South Korean importer using USD.

This techno-ideological divide is mirrored in their functional roles. CIPS is an integrated messaging and Real-Time Gross Settlement engine supervised by the People’s Bank of China. It does not merely transmit communications; it executes the final balance-sheet movement of funds in Renminbi within a single sovereign architecture. SWIFT, by contrast, is a universal financial messaging cooperative that holds no funds and settles no accounts. It delivers standardized transaction instructions, leaving the actual netting and real-time final settlement (when needed) to domestic clearing systems, specifically CHIPS and Fedwire in New York.

These mechanical differences shape entirely distinct trade dynamics across the globe. CIPS fosters a bilateral, radial pattern of trade where nations selling commodities or raw materials to China accumulate RMB balances, which they must then recycle into Chinese manufactured exports, industrial equipment, or state engineering contracts. Value flows inward toward and outward from the central Chinese node in Beijing.

The USD-SWIFT system drives multilateral, distributed trade, where companies hold dollar liquidity because it can be deployed anywhere in the world to buy energy and other commodities, settle contracts, or invest in third-party markets without restriction.

This structural divergence is enforced by the degree of capital account openness in each home country. China operates a managed and restricted capital account, using strict cross-border controls to insulate its domestic financial system from external volatility and preserve monetary independence. The US system rests on a fully open capital account, underpinned by the multi-trillion-dollar US Treasury market, which provides global central banks and institutions with the deepest, most liquid secondary market in history.

Inevitably, each architecture carries its own defining structural vulnerability. For CIPS and the RMB, the primary bottleneck is trapped surpluses where foreign counterparties accumulate non-convertible currency that cannot easily be redeployed outside trade with China.

For SWIFT and the USD, the vulnerability is sanction weaponization, using global clearing access and messaging cutoffs as tools of geopolitical coercion. This has incentivized non-aligned nations to build parallel financial circuits, trading the immense liquidity of an open commons for the political insulation of a sovereign hub.

In sum, CIPS is not simply a replacement for SWIFT. It is better understood as an attempt to build a China-centered payment and settlement infrastructure around the renminbi, while still using substantial parts of the existing global financial architecture. That distinction actually makes the geopolitical argument more interesting.

CIPS, SWIFT, and the Politics of Financial Infrastructure

The US dollar has supplied much more than a currency. It has supplied a global infrastructure for trade, credit, settlement, liquidity, and price discovery. SWIFT is only one component of that infrastructure. Dollar clearing, correspondent banking, CHIPS, Fedwire, Treasury markets, Eurodollar lending, FX markets, and the institutions surrounding them form a much larger system. Its extraordinary value comes from network effects. Companies in Vietnam, Mexico, Bangladesh, Brazil, Nigeria, Germany, and China can transact with one another without constructing a separate bilateral monetary system for every trading relationship.

That is one reason the dollar has been such an important enabling infrastructure for global development. A Vietnamese exporter does not have to trust the Vietnamese dong to trade with Mexico; a Bangladeshi manufacturer does not need to hold pesos to sell to a Mexican buyer. Dollar liquidity provides a common intermediate medium through which enormous numbers of otherwise unrelated transactions can be coordinated.

CIPS is not SWIFT 2.0

China created CIPS in 2015 to promote cross-border renminbi settlement and internationalize the RMB. It has grown substantially. By the end of 2025, CIPS reported 193 direct participants and 1,573 indirect participants across 124 countries and regions, with its broader banking network reaching roughly 190 countries.

But the distinction between payment settlement and financial messaging matters. CIPS is a settlement system, whereas SWIFT is primarily a messaging network. Moreover, CIPS remains interconnected with SWIFT and the existing international financial system. The US-China Economic and Security Review Commission noted that CIPS still relies heavily on SWIFT messaging while maintaining its own messaging capability for direct participants.

This suggests that China’s strategy is not to destroy SWIFT. It is to construct a parallel RMB-centered financial geography that can operate with less dependence on US-controlled infrastructure when necessary. That is a rational strategy from Beijing’s perspective. The problem is what happens when the payment network becomes part of a larger system of economic dependence.

The Lesson of Russian Energy

Europe’s experience with Russian energy provides an important analogy. For decades, Europe benefited enormously from Russian natural gas. The arrangement was economically efficient as Russia supplied relatively inexpensive energy while European industries and consumers received dependable fuel. But the invasion of Ukraine demonstrated that economic interdependence can become geopolitical leverage.

The European Commission subsequently described Russia’s energy exports as having been “weaponised” and embarked on REPowerEU to diversify supplies, reduce fossil-fuel consumption, and eliminate excessive dependence on Russian energy.

The lesson was not that Russian gas was technologically inferior. Quite the opposite. It was economically attractive precisely because the infrastructure was deeply integrated. The problem was that integration created vulnerability when the supplier possessed political objectives that could conflict with the interests of the customer.

This is the crucial question for CIPS. If a country such as Bangladesh, Vietnam, Mexico, Indonesia, or another developing economy increasingly conducts trade through a Chinese-controlled monetary infrastructure, it may gain cheaper access to RMB liquidity and Chinese markets. But it could also acquire a new form of dependency.

The concern is not necessarily that Beijing would immediately “control” these economies. That would be too strong. Rather, the architecture could give China greater leverage over the conditions under which economic relationships operate.

Payment infrastructure can influence who can transact, which currencies can be used, which banks can participate, how compliance is performed, how information moves, and ultimately which economic relationships are easiest or most difficult to maintain.

USD dependence to Infrastructure Dependence

This is where the comparison with the dollar becomes particularly revealing. The dollar system also possesses enormous power. The United States can use sanctions, export controls, financial restrictions, and access to dollar clearing as instruments of statecraft. That power should not be minimized. But an important difference exists between a globally distributed infrastructure and a nationally centered infrastructure.

The dollar system has become extraordinarily useful precisely because participants from many countries can use it without becoming economically subordinate to the United States in every other respect. A Mexican manufacturer can trade with a Vietnamese supplier. A Bangladeshi garment exporter can receive dollars from an American retailer. A Brazilian commodity producer can sell to China. A Nigerian company can purchase equipment from Europe.

The dollar functions as a kind of common computational and monetary language. That does not make it politically neutral. It makes it infrastructurally universal.

CIPS offers something different. It is an alternative monetary infrastructure centered on China’s currency, banking system, and geopolitical relationships. As its network expands, it could become increasingly useful for countries wishing to reduce exposure to US sanctions and dollar clearing. The US Congressional research and security literature explicitly identifies this sanctions-resilience function as one reason CIPS matters.

This creates a paradox. The world may want a more multipolar monetary system because excessive dependence on one country creates vulnerabilities. But replacing one dominant network with several competing monetary blocs can increase transaction costs.

Imagine a world divided among dollar, RMB, euro, rupee, and perhaps regional digital-currency systems. Every multinational corporation would need to manage multiple liquidity pools, payment systems, compliance regimes, exchange-rate exposures, collateral arrangements, and settlement infrastructures. The result could be less global liquidity, not more.

This is particularly important for developing economies. Their principal problem has historically not been a lack of currencies. It has been a lack of access to deep, liquid, internationally accepted currencies. Note the different circumstances faced by countries in the tiered global USD system.

The dollar’s great infrastructural advantage is that it allows countries to participate in global markets without having to possess currencies that are themselves globally trusted.

The SACT Interpretation

The global spreadsheet logic/dollar system can be understood as a gigantic coordination system I call the Substitution-Abstraction-Symbolic Computing-Telecom Synchronization (SACT) stack. Substitution replaces innumerable bilateral monetary relationships with a common settlement medium. Abstraction converts heterogeneous national currencies, commodities, contracts, and financial claims into interoperable currency-denominated units. Symbolic computation allows those units to be priced, collateralized, netted, cleared, and redistributed through financial institutions and markets. Telecommunications synchronization connects the resulting financial states across borders.

SWIFT, CHIPS, Fedwire, correspondent banks, Treasury markets, FX markets, and the Eurodollar system therefore constitute something considerably larger than a payment network. They form a global monetary information infrastructure.

CIPS is an attempt to construct an alternative version of that infrastructure. The geopolitical question is therefore not simply “Will CIPS replace SWIFT?” It is which financial infrastructure will provide the computational grammar through which global economic activity is coordinated?

And this brings us back to the Russian energy analogy. Europe eventually concluded that a highly efficient infrastructure could become dangerous when excessive dependence on one supplier created political vulnerability. The EU’s post-2022 policy explicitly emphasized diversification and resilience rather than simply replacing Russian gas with another single source. That may be the more useful lesson for monetary infrastructure as well. The future should be interoperable, not bipolar.

The answer to CIPS probably should not be an attempt to preserve an exclusive American monopoly over international payments. Nor should the world simply substitute Chinese monetary infrastructure for American infrastructure. The better objective is interoperability without political capture.

Treasury-backed dollar stablecoins could potentially become an important part of that architecture. Rather than requiring every country to construct a separate correspondent-banking system, regulated digital dollars could provide globally accessible dollar liquidity through mobile wallets and blockchain settlement networks. At the same time, interoperability with other currencies and payment systems could prevent the emergence of another closed monetary bloc.

The ultimate competition, then, is not between SWIFT and CIPS. It is between open global liquidity and politically conditioned liquidity. The dollar’s historical advantage has been that its infrastructure became so widely distributed that it ceased to look like an American product and became part of the operating environment of world commerce. CIPS is increasingly important because China wants a greater measure of control over that environment.

The central challenge for the next monetary order is therefore to preserve the extraordinary network effects that made global trade possible and affordable while preventing any single state from turning financial infrastructure into an instrument of dependency. That is perhaps the strongest argument for extending dollar liquidity, not simply preserving the existing dollar system, but making dollar liquidity more distributed, digital, interoperable, and accessible to a developing world.

Notes

[1] I started this inquiry in my Master’s thesis (August 1986) on SWIFT and other technological innovations that emerged in the late 1970s and early 1980s. I recently decided to compare SWIFT with new Chinese fintech innovations, specifically looking at which would provide a global commons as enabling infrastructure for global development. Interestingly, at the time the name for SWIFT was Society for Worldwide Interbank Funds Transfer.
AI Prompt(s) Describe the competition between CIPS replaces SWIFT. The USD has been the enabling infrastructure for global development. Make the argument that CIPS is just a way for China to control its competitors such as Bangladesh, Mexico, and Vietnam. Remember why Europe rejected Russian energy. Too many strings attached.

Category: dystopian economies, enabling frameworks, enterprise systems, financial technology, geopolitical risk
Tags: China’s Cross-Border Interbank Payment System (CIPS) > CIPS > Cross-Border Interbank Payment System (CIPS) > Dedollarization > Global Public Good > Public Good > Substitution-Abstraction-Symbolic Computing-Telecom Synchronization (SACT) > The Society for Worldwide Interbank Financial Telecommunications (SWIFT)

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About Me

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

You can reach me at:

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anthony.pennings@sunykorea.ac.kr

Anthony J. Pennings, PhD

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CIPS vs. SWIFT: Dedollarization or Global Public Good?

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