This article is an introduction to an economic monetary theory paper and a substitute article of the paper written for the laymen. Read the full, scholarly economic monetary theory paper here.

For most of modern history, money has never been just money. It has been power, leverage, deterrence, and—when designed correctly—a substitute for war. The current debate around Bitcoin, central bank digital currencies, and the future of the dollar is often framed as a technological or ideological dispute. In reality, it is something far older and far more consequential. It is a debate about the structure of world order.

The digitization of money is no longer speculative. What remains unresolved—and increasingly determinative of global stability—is which monetary architecture will govern digital settlement at scale. As the U.S. dollar’s effectiveness as an instrument of economic statecraft has eroded since the collapse of Bretton Woods, the United States now confronts a strategic fork: whether the next global settlement layer remains anchored in sovereign monetary infrastructure or migrates toward a non-sovereign protocol such as Bitcoin. This is not a question of payments efficiency or innovation for its own sake. It is a question about deterrence, escalation, and whether financial power can continue to prevent wars rather than accelerate them.

To understand why this matters, it helps to revisit how monetary power has functioned historically. The classical gold standard of the late nineteenth and early twentieth centuries constrained state discretion by tying currencies to a physical commodity. That system produced price stability, but it collapsed under the fiscal demands of total war. States could not fight industrialized wars while remaining bound by convertibility. The breakdown of the gold standard during World War I made this clear.

Bretton Woods was an attempt to solve that problem. The postwar order replaced pure gold convertibility with an institutional architecture anchored to the U.S. dollar, itself convertible to gold, and embedded within new international institutions. This was not merely an economic arrangement; it was a geopolitical one. The United States used its industrial dominance and financial capacity to underwrite reconstruction and security, while allies accepted U.S. monetary leadership in return for stability (Helleiner 2014).

When gold convertibility finally became unsustainable and the Nixon administration suspended it in 1971, many expected the dollar’s dominance to collapse. Instead, it evolved. The anchor shifted from gold to credibility. Deep U.S. Treasury markets, the dollar’s role in global trade, and the pricing of key commodities—most notably oil—in dollars preserved demand for dollar settlement. The so-called petrodollar system reinforced this arrangement, ensuring that global energy markets continued to clear in dollars (Spiro 1999). What emerged was not a rules-based metallic system, but an institutional one.

Over time, this system enabled something new: financial statecraft. Control over dollar clearing, correspondent banking, and settlement infrastructure allowed the United States and its allies to impose costs without firing shots. Sanctions became a substitute for kinetic force. As Henry Kissinger’s broader theory of balance-of-power politics implies, stable orders rely on structures that constrain behavior short of war (Kissinger 1994). In the late twentieth and early twenty-first centuries, dollar-centric finance became one such structure.

This is what political economists Henry Farrell and Abraham Newman later described as “weaponized interdependence.” Control over key nodes in global networks—financial messaging, clearing, settlement—allowed states to coerce others not through invasion, but through exclusion (Farrell and Newman 2019). Monetary power became deterrence.

But deterrence only works if it is credible. And in the past decade, cracks have appeared. The expanded use of sanctions, particularly after Russia’s annexation of Crimea in 2014, has revealed both their strength and their limits. The Russia–Ukraine war made those limits explicit. Cryptographic payment rails and digital assets created alternative pathways for cross-border value transfer, particularly for actors already excluded from legacy systems. These rails do not replace the dollar, but they weaken the completeness of enforcement. Even partial evasion matters, because it alters incentives. When financial pressure becomes porous, states are more likely to resort to force.

U.S. institutions are aware of this. Treasury and Federal Reserve reports now explicitly frame payment infrastructure as national-security infrastructure, not merely a consumer or fintech issue (U.S. Department of the Treasury 2022; Board of Governors of the Federal Reserve System 2022). The question is no longer whether money will digitize, but who controls the settlement layer when it does.

This is where the Bitcoin versus U.S. central bank digital currency debate becomes consequential. If Bitcoin becomes the de facto global reserve settlement layer, the result is a structurally multipolar financial order. Bitcoin’s neutrality, fixed supply, and censorship resistance eliminate discretionary enforcement. While transactions are traceable, traceability without enforceability does not constitute state power. In a Bitcoin-centric settlement world, sanctions lose credibility, financial coercion weakens, and escalation increasingly shifts from economic to kinetic domains. Multipolarity may sound appealing in theory, but historically it has correlated with higher instability and conflict.

By contrast, a U.S. central bank digital currency preserves U.S. and allied hegemony by upgrading the dollar’s role as the backbone of global finance. A sovereign digital settlement rail enables programmable compliance, faster cross-border payments, and more targeted enforcement. This distinction is not moral but functional. Bitcoin is for the people. A USCBDC is for the state.

That separation—between a domestic autonomy rail and an external sovereignty rail—is not unprecedented. History offers a revealing stress test in interwar Germany. Facing severe foreign-exchange constraints and an acute “transfer problem,” Germany implemented a dual-track settlement system in the 1930s. Domestically, the Reichsmark circulated under tight controls. Externally, settlement was segmented through exchange controls, blocked mark accounts, and special foreign accounts known as Ausländer Sonderkonten, or Aski accounts. These instruments were legally non-convertible and usable only for restricted trade purposes, often within bilateral clearing arrangements (U.S. Department of State 1936; International Military Tribunal 1946).

In effect, Germany created different kinds of “money” for different purposes without changing the unit name. Economic historians agree on the trade-offs. In the short run, segmentation conserved scarce foreign exchange and restored state control over external settlement. It allowed authorities to prioritize strategic imports and discriminate among counterparties. But the costs were severe. Non-convertibility produced discounts and shadow exchange rates. Bilateral clearing increased friction relative to multilateral settlement. Segmentation incentivized evasion and black markets. Over time, reputational damage raised risk premia and motivated counterparties to seek exit options (Accominotti et al. 2023; Faudot 2020).

The lesson is not that dual-rail systems are inherently flawed. The lesson is that opaque, discretionary segmentation is unstable. When conversion rules are unpredictable and enforcement is arbitrary, the system collapses into evasion, discounting, and fragmentation.

This is the critical insight for modern policy. A future architecture in which Bitcoin serves as a domestic autonomy rail and a USCBDC serves as the cross-border settlement rail can work—but only if conversion between the two is governed by transparent, rule-bound gateways rather than ad hoc discretion. Control must be exercised at regulated intermediaries and borders, not at the level of everyday domestic transactions. This is precisely why Federal Reserve and Treasury design principles emphasize intermediated CBDCs rather than anonymous retail bearer instruments (Board of Governors of the Federal Reserve System 2022; U.S. Department of the Treasury 2022).

This framing also clarifies why proposals for the United States to adopt Bitcoin itself as a reserve instrument are strategically incoherent. No hegemonic power voluntarily anchors its monetary system to a protocol it cannot modify, suspend, or adapt in crisis. Doing so would permanently surrender lender-of-last-resort capacity, eliminate discretionary response to shocks, and forfeit financial statecraft. That outcome would not democratize global finance. It would fragment it—and remove one of the last non-violent tools for managing escalation.

Seen through this lens, recent oscillations in U.S. political rhetoric on CBDCs are not ideological confusion. They are strategic recalibration. As rival powers develop alternative digital settlement systems, abstention becomes indistinguishable from retreat. Monetary architecture has become a domain of geopolitical competition.

The choice before us, then, is not between freedom and control. It is between order and fragmentation. Monetary systems shape incentives long before conflicts turn kinetic. If financial power continues to provide a credible alternative to war, stability can persist even amid rivalry. If that power dissolves into neutral protocols without enforceable settlement, conflicts that could have been mediated economically will increasingly be resolved by force.

This essay is an introduction to a formal monetary economic theory paper that develops these arguments using economic theory, mechanism design, and game-theoretic modeling. For readers interested in the technical details—constraints, equilibria, and policy design—the full paper is linked below. But the core claim is simple: in a digital age, peace still depends on architecture. And money, as it always has, sits at the center of that architecture.

Read the full, scholarly economic monetary theory paper here.

References

Accominotti, Olivier, Thilo Albers, Volker Kessler, and Kim Oosterlinck. 2023. “The Political Economy of the German Default in the 1930s.” Working paper.

Board of Governors of the Federal Reserve System. 2022. Money and Payments: The U.S. Dollar in the Age of Digital Transformation. Washington, DC.

Farrell, Henry, and Abraham L. Newman. 2019. “Weaponized Interdependence: How Global Economic Networks Shape State Coercion.” International Security 44 (1): 42–79.

Faudot, Adrien. 2020. “Multilateral Clearing from Theory to Practice: The Deutsche Verrechnungskasse during World War II.” University of Oxford.

Helleiner, Eric. 2014. Forgotten Foundations of Bretton Woods: International Development and the Making of the Postwar Order. Ithaca, NY: Cornell University Press.

International Military Tribunal. 1946. Nazi Conspiracy and Aggression, vol. 2. Washington, DC: U.S. Government Printing Office.

Kissinger, Henry. 1994. Diplomacy. New York: Simon & Schuster.

Spiro, David E. 1999. The Hidden Hand of American Hegemony: Petrodollar Recycling and International Markets. Ithaca, NY: Cornell University Press.

U.S. Department of State. 1936. “The Foreign Exchange Clearing Methods Used in Commerce With the United States.” Foreign Relations of the United States, 1936, Europe, vol. II.

U.S. Department of the Treasury. 2022. The Future of Money and Payments: Report Pursuant to Section 4(b) of Executive Order 14067. Washington, DC.

Civilization has always advanced through the reorganization of constraint. Each new phase of growth emerges not from abundance but from ingenuity under pressure. For millennia, humanity lived inside the logic that Thomas Malthus described: when population grows faster than production, prosperity collapses back toward subsistence. Wages rise after a good harvest, fertility responds, and the gains dissolve. The equilibrium is cruelly efficient. It is also stable (Malthus 1798; Clark 2007). What shattered that equilibrium was not fortune but knowledge. The printing press, mechanization, electricity, and computation each altered the relationship between population, resources, and output. Each wave of technology increased the productivity of both labor and capital, widening the space between what human beings could imagine and what their environment could sustain.

In modern growth theory, that widening space is measured as productivity—the efficiency with which capital and labor combine to produce output. Robert Solow showed that even when a society accumulates more machinery or adds workers, long-run income per person rises only through technological progress. When technology improves, output can grow faster than inputs. The challenge has always been to understand what makes technology improve. Early models treated innovation as something that arrived from outside the system, as if progress were a force of nature. Later theorists such as Paul Romer and Philippe Aghion pulled it inside the model of the economy itself, showing that invention and learning arise from human effort, incentives, and institutional design (Romer 1990; Aghion and Howitt 1992).

The simplest way to describe exponential growth is to say that knowledge creates the means for further knowledge. Machines build better machines. Ideas generate tools that expand the range of possible ideas. In older economies, labor and capital were distinct. Today, that boundary is dissolving. Automation turns capital into labor by performing tasks once reserved for humans, while artificial intelligence turns labor into knowledge by embedding reasoning into work itself. The rate of progress, once dependent on slow human diffusion, accelerates as technology begins to improve its own capabilities. Each innovation enhances the system’s capacity to produce further innovation, creating a feedback loop of compounding advancement.

This is the moment we are entering. The convergence of artificial intelligence, agentic architectures, robotics, and distributed automation represents not merely another industry or toolset, but a fundamental reorganization of production. These technologies amplify the productivity of every other factor by embedding intelligence directly into the processes of creation, allocation, and maintenance. The factory, the laboratory, the logistics chain, and even the administrative office begin to think. What emerges is not automation alone, but the partial self-optimization of the economic system.

Traditional models assume that as an economy accumulates capital and knowledge, it faces diminishing returns. Each additional investment yields slightly less than the last. But when machines can improve their own performance and replicate their intelligence, the rule changes. The efficiency of combining resources—what economists call total factor productivity—ceases to be fixed. It becomes an evolving function of technology itself. Every new generation of learning systems increases the efficiency of those that follow. Software refines software. Engineering learns to redesign itself. The result is not a mechanical multiplier but a recursive one, a state in which the process of producing value also produces better ways to produce value.

Evidence of this transition already appears in data. Studies of generative AI in the workplace find that access to large language models significantly increases output per hour, with the largest gains among less experienced workers (Brynjolfsson, Li, and Raymond 2025). Controlled experiments show that knowledge workers complete tasks faster and at higher quality when assisted by generative systems (Noy and Zhang 2023). These results describe only the first wave, limited to writing, coding, or customer service. As these tools integrate into supply chains, research pipelines, and physical industries, the productivity effects will compound. Electricity first illuminated workshops before reorganizing the entire factory. Computers began as calculating aids before becoming coordination engines. AI, agentic software, and robotics will follow that same pattern, moving from localized efficiency to systemic transformation.

The logic of growth therefore shifts again. Malthus described a world limited by arithmetic subsistence. The industrial revolution replaced that constraint with technological progress but still required human governance to diffuse and direct innovation. The algorithmic era introduces something unprecedented: technological progress that is partially autonomous. The central question becomes how to ensure that self-improving systems remain aligned with collective welfare rather than uncoordinated acceleration.

In the postwar decades, economists spoke of the Solow residual—the unexplained portion of growth attributed to technology. It was the measure of what we did not understand, the contribution of knowledge and organization to prosperity. In the coming era, that residual will no longer be mysterious. Artificial intelligence will make productivity itself measurable and programmable. The economy will not merely use knowledge; it will learn.

This is where the concept of agentics matters. Agentic systems are software entities that act autonomously toward defined goals, equipped with reasoning, memory, and adaptive behavior. When integrated with robotics, they become physical agents capable of perception, planning, and execution in the real world. A network of such entities can coordinate logistics, negotiate contracts, and manage resources without continuous human supervision. Imagine a supply system in which autonomous vehicles, drones, and schedulers cooperate to deliver goods based on real-time data on demand, traffic, and energy use. Each component acts as both participant and administrator. The economy begins to function as a distributed intelligence.

The effect on coordination costs is transformative. Ronald Coase observed that firms exist because they reduce the transaction costs of operating in markets (Coase 1937). Bureaucracies arose to process information where markets were too slow or noisy. But when computation makes coordination instantaneous, the logic of organization changes. The same principle that allowed markets to supersede feudal economies now applies to algorithmic coordination. Artificial agents can negotiate, allocate, and enforce agreements at speeds beyond human capability. The invisible hand becomes computational.

This transformation expands what economists call effective capital: not only physical machinery or human labor, but the accumulated intelligence embodied in systems. Once intelligence can be replicated at near-zero cost, the supply of productive capacity becomes effectively infinite. The constraints on growth shift from human population and capital stock to computation and energy. The potential for exponential expansion arises when each unit of energy or computation yields greater value through smarter allocation and learning.

Yet exponential potential does not automatically translate into social benefit. In a system where automation deepens faster than labor can adapt, income and power can concentrate. Historically, institutional innovation resolved similar tensions. The industrial age required new social and legal mechanisms—public education, social insurance, and regulatory states—to distribute gains and stabilize progress. The algorithmic age will require a different response: the reinvention of institutions themselves.

Institutions are the steering systems of civilization. They stabilize expectations so that individuals can act without perfect knowledge of others. Markets, firms, legal systems, and governments perform this function in different ways. What changes now is the mechanism of stability. Instead of human intermediaries interpreting information and enforcing compliance, algorithmic systems can embed norms directly into logic. Smart contracts, digital ledgers, and regulatory agents can verify, audit, and execute automatically. This eliminates layers of administration but introduces a new dimension of design: the constitution of code.

Economic historians like Douglass North and Daron Acemoglu have shown that the quality of institutions determines how effectively societies convert innovation into growth (North 1990; Acemoglu, Johnson, and Robinson 2001). Secure property rights, open markets, and accessible knowledge transformed invention into prosperity. The next step is to translate those same functions into computational form. A smart contract becomes a property right expressed as algorithmic rule. A decentralized organization becomes a firm expressed as logic. A reputation system becomes a market in trust. Each represents a piece of governance rendered into computation. Together, they form the scaffolding of what may become an algorithmic republic.

The risk, of course, is that such systems reproduce hierarchy at machine speed if their objectives are poorly designed. Algorithms that maximize engagement can polarize discourse; those that optimize efficiency can externalize costs to workers or the environment. The challenge is constitutional. Optimization must be bounded by transparency, auditability, and reversibility. Just as constitutional democracy once constrained rulers through checks and balances, the algorithmic republic must constrain optimization through deliberate design.

When constructed wisely, algorithmic institutions can do what bureaucracies cannot. They can allocate resources in real time, price externalities dynamically, and adjust policy with minimal lag. Energy grids already balance demand through algorithmic feedback. Similar architectures could manage emissions quotas, adaptive taxation, or real-time fiscal transfers. The economy becomes a continuous control system, estimating and correcting itself through recursive learning. In growth terms, this is equivalent to raising the efficiency of technology itself by minimizing waste and misallocation. The more precisely resources flow to their highest use, the faster productivity compounds.

Romer’s insight that ideas are nonrival—capable of being shared without depletion—applies with greater force in this context. When knowledge is embedded in code, it can be replicated endlessly and improved collectively. Each deployment adds to a self-expanding stock of intelligence. Growth becomes truly endogenous, arising not only from human invention but from the recursive refinement of the coordination mechanisms that govern invention.

Still, exponential coordination demands moral architecture. Michel Foucault noted that modern power produces subjects rather than merely repressing them (Foucault 1977). Algorithmic power extends that dynamic by predicting before it commands. Systems that anticipate our actions can steer outcomes without overt coercion. In such a world, freedom depends on maintaining unpredictability within prediction. Autonomy becomes the right to introduce uncertainty into models that would otherwise close over the future. Economically, this unpredictability sustains innovation; politically, it sustains liberty.

The design of the algorithmic republic therefore requires a synthesis of economics, law, and behavioral science. Economically, it must preserve creative destruction, ensuring that intelligent systems enhance rather than entrench incumbents. Legally, algorithmic authority must remain delegated, accountable, and reversible. Behaviorally, citizens must understand the architectures that shape their attention and choices. Only then can they participate meaningfully in the design of systems that increasingly anticipate their behavior. These dimensions correspond to innovation, inclusion, and information integrity—the three foundations of sustainable growth.

In the macroeconomic sphere, algorithmic coordination will change the nature of stability itself. Monetary and fiscal policy, once slow and politically constrained, could become adaptive and continuous. Digital currencies governed by algorithms might adjust liquidity in response to real-time indicators. Automated fiscal systems could distribute transfers or infrastructure funding automatically based on verified data. The result would be an economy capable of self-correction within narrow bounds rather than swinging between crisis and boom.

Yet interconnection carries new risks. When algorithms coordinate every sector, errors and biases can propagate instantaneously. The safeguard is diversity of design. Just as biodiversity stabilizes ecosystems, model plurality stabilizes complex economies. Multiple systems, each trained on distinct data and objectives, create resilience through heterogeneity. Regulation should focus less on constraining individual models and more on ensuring diversity, transparency, and contestability across the entire coordination layer.

Under these conditions, freedom and growth become mutually reinforcing. Societies that protect cognitive and institutional pluralism will learn faster and adapt better. Authoritarian optimization may deliver short-term efficiency but sacrifices long-term dynamism. The most prosperous algorithmic republic will be one that institutionalizes dissent, preserving the capacity for contradiction as a source of learning. The right to remain partially opaque to prediction becomes both a civil liberty and a source of comparative advantage.

The transition will not happen at once. Human bureaucracies will coexist with autonomous systems, hybrid structures where oversight and computation share control. The critical step will be to translate legal and ethical principles into machine-readable constraints. Due process, proportionality, and fairness must become design features rather than afterthoughts. No algorithmic authority should be absolute or permanent. Each must carry within it the capacity for revision, audit, and appeal.

The final measure of success will not be technical performance alone, but whether exponential growth aligns with human flourishing. Productivity gains can raise incomes and expand opportunity only if access to computation, education, and data governance evolves alongside efficiency. Universal computational infrastructure and algorithmic literacy can ensure that recursive productivity becomes a shared benefit. If done correctly, the algorithmic republic could achieve what no previous system has managed: abundance without exclusion, coordination without coercion, and growth without exhaustion.

The same intelligence that once appeared to threaten human agency can become its guardian if built with foresight. The institutions of the future will not reside in marble halls but in code—auditable, updateable, and open to contestation. The task of our century is to write those constitutions wisely, embedding freedom within optimization and aligning intelligence with purpose. The age of autonomous institutions has begun, and it will test whether civilization can create not only smarter systems but a wiser world.

Share

References

Acemoglu, Daron, Simon Johnson, and James A. Robinson. “The Colonial Origins of Comparative Development: An Empirical Investigation.” American Economic Review 91, no. 5 (2001): 1369–1401.

Aghion, Philippe, and Peter Howitt. “A Model of Growth through Creative Destruction.” Econometrica 60, no. 2 (1992): 323–351.

Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond. “Generative AI at Work.” Quarterly Journal of Economics 140, no. 2 (2025): 889–930.

Clark, Gregory. A Farewell to Alms: A Brief Economic History of the World. Princeton: Princeton University Press, 2007.

Coase, Ronald. “The Nature of the Firm.” Economica 4, no. 16 (1937): 386–405.

Foucault, Michel. Discipline and Punish: The Birth of the Prison. New York: Pantheon Books, 1977.

Malthus, Thomas R. An Essay on the Principle of Population. London: J. Johnson, 1798.

North, Douglass C. Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge University Press, 1990.

Noy, Shakked, and Whitney Zhang. “Experimental Evidence on the Productivity Effects of Generative AI.” Working paper, 2023.

Romer, Paul M. “Endogenous Technological Change.” Journal of Political Economy 98, no. 5 (1990): S71–S102.

Solow, Robert M. “A Contribution to the Theory of Economic Growth.” Quarterly Journal of Economics 70, no. 1 (1956): 65–94

The statement that “Malthus was wrong” is often repeated with a sense of triumph, but it reflects a misunderstanding of what Thomas Robert Malthus actually observed. For most of human history, the logic he described was painfully accurate. Populations grew more quickly than the means of subsistence, pressing wages and living standards back toward a steady state of scarcity. Real incomes fluctuated with the harvest, technology, and disease, but across centuries, the average worker lived and died close to subsistence (Malthus 1798; Clark 2007). The true mystery of economic history is not Malthus’s pessimism, but the fact that his world eventually disappeared. Understanding how humanity escaped the Malthusian trap remains essential for understanding why growth happens at all and why it sometimes stops.

If we look across time, the pattern is clear: societies spend long stretches in near-equilibrium, with living standards tied to resource constraints, punctuated by rare and transformative breakthroughs that reshape production and knowledge. These are what might be called “productivity spurts,” moments when new general-purpose technologies (GPTs) such as the printing press, the spinning wheel, the steam engine, or the microprocessor drastically increase the efficiency of work and the reach of human knowledge. In these moments, the basic arithmetic of scarcity temporarily breaks. When productivity rises faster than population or resource depletion, the Malthusian logic gives way to sustained gains in welfare. Yet this escape is never permanent. Every wave of progress eventually encounters limits, and the world settles again into a slower, more constrained phase until another major technology emerges. Economic growth, in this view, is not linear. It is phasal. The natural condition of human economies is Malthusian equilibrium; the bursts of progress are the exceptions that must be continually recreated (Clark 2007; Galor 2011).

To appreciate the profundity of this shift, it helps to start where Malthus did. In the pre-industrial world, productivity gains were typically erased by population growth. Suppose an English farmer invented a more efficient plow or had a few good harvests. The extra income would allow earlier marriages and more children. Within a generation, the population would rise, the land would be subdivided, and wages would fall again. The average person lived only slightly better than their ancestors did centuries before. Epidemics, wars, and famine periodically reduced populations, allowing temporary recovery, but the long-run pattern was flat. Global GDP per capita barely changed for millennia. It is difficult for modern observers to comprehend how stable that equilibrium was. Malthus’s insight, that population pressure could permanently constrain prosperity, was not pessimistic for his time; it was descriptive (Malthus 1798; McNeill 1976).

And yet, history did eventually bend. Somewhere between the fifteenth and nineteenth centuries, the equilibrium cracked. For the first time, the relationship between population and subsistence loosened. The world shifted from a static economy to a dynamic one. To understand how that happened, we must examine how information and technology interacted with institutions to change the rules of the game.

The story begins not with steam, but with ink. When Johannes Gutenberg perfected movable type in the mid-fifteenth century, he did more than revolutionize communication. He reduced the marginal cost of reproducing information by orders of magnitude. A single press could produce hundreds of identical copies of a text in the time it once took a monk to copy one by hand. This lowered the barriers to entry for knowledge, which had previously been guarded by religious and political elites. The printing press democratized learning. Within fifty years of Gutenberg’s invention, presses had spread to over two hundred European cities (Eisenstein 1979; Dittmar 2011).

The economic effects were profound. Cities that adopted the printing press early saw faster population growth, higher literacy, and greater innovation in subsequent centuries (Dittmar 2011). Ideas that once took years to circulate could now spread in months. Technical manuals, scientific treatises, and commercial guides diffused through Europe’s growing merchant networks. Knowledge compounded. The Protestant Reformation, the Scientific Revolution, and the Enlightenment all relied on this infrastructure of cheap information (Eisenstein 1979; Mokyr 2002). Knowledge is not only a cultural force but an economic one: it is the ultimate input into productivity. When more people can access, test, and improve on prior ideas, innovation accelerates. The printing press was, in this sense, an early general-purpose technology of information, and it paved the way for industrialization.

But technology alone was not enough. Many societies invented or adopted new tools, only to stagnate. What distinguished the countries that escaped the Malthusian trap was not only what they invented but how they organized themselves. Institutions—the formal and informal rules that govern incentives—determined whether new technologies translated into broad-based growth. As Douglass North famously wrote, institutions are the “rules of the game” that shape economic performance (North 1990). When those rules protect property rights, reward innovation, and allow entry, growth compounds. When they are extractive, protecting elite rents and suppressing competition, innovation stalls.

Empirical history supports this. Acemoglu, Johnson, and Robinson (2001) showed that colonies where Europeans settled with inclusive property regimes, such as in North America, developed sustainably higher incomes than those where extractive institutions dominated, as in parts of Latin America and Africa. Similarly, the divergent paths of North and South Korea in the twentieth century reveal how political and institutional design determine technological diffusion. The two Koreas share geography, language, and culture, but the South’s inclusive, market-oriented institutions produced one of the most advanced economies on Earth, while the North’s closed, extractive system remains impoverished. The same logic applied centuries earlier. Where governments protected intellectual freedom and competition, the press, and later industrial machinery, flourished. Where censorship and monopolies prevailed, progress slowed.

The Industrial Revolution was therefore not simply a mechanical event but an institutional one. It required a combination of accessible knowledge, inventive activity, and governance structures that allowed entrepreneurial experimentation. James Watt’s improvements to the steam engine, for example, built on a long lineage of published research and practical tinkering. Britain’s patent system and relatively open financial markets allowed inventors to commercialize ideas and attract investment. Schumpeter later called this process “creative destruction”: innovation destroys old equilibria and creates new ones (Schumpeter 1942). But destruction without inclusion can just as easily create oligarchy. Inclusive institutions, which distribute opportunity and ensure competition, are what keep the system dynamic rather than brittle.

By the mid-twentieth century, economists began to formalize what had long been implicit in history. Robert Solow’s neoclassical growth model (1956) demonstrated that capital and labor accumulation alone could not sustain long-term growth because of diminishing returns. Only technological progress—an exogenous factor in his model—could explain persistent increases in output per worker. Paul Romer later internalized that factor. His theory of endogenous growth (1990) treated technology as the product of purposeful economic activity, subject to incentives, institutions, and ideas. Technological change arises from investments in human capital and research, and it can be influenced by policy.

More recently, unified growth theory (Galor 2011; Kremer 1993) has woven these threads together. It describes a long Malthusian phase where population growth absorbs economic gains, followed by a demographic transition where falling fertility allows per-capita income to rise, and finally a modern growth regime sustained by innovation and education. The shift between these phases occurs when the rate of technological progress exceeds the rate at which population growth erodes gains. In other words, growth depends not only on invention but on the social and demographic conditions that let it accumulate.

Each major technological era since Gutenberg has followed a similar pattern: new means of information production lead to wider participation in knowledge creation, which fuels innovation, which in turn supports new institutions and further growth. The digital revolution of the twentieth century was an extension of this sequence. Computers, telecommunications, and later the internet exponentially reduced the cost of transmitting, storing, and replicating data. The diffusion of knowledge became nearly frictionless. Yochai Benkler (2006) described this as the rise of “commons-based peer production,” where open collaboration and information sharing enabled large-scale innovation without traditional market hierarchies.

Yet the digital era also introduced new pathologies. The same networks that democratized access to information also amplified misinformation, polarization, and manipulation. Studies show that false or sensational information spreads faster than accurate news on social media (Vosoughi, Roy, and Aral 2018). The abundance of data has not always translated into abundance of wisdom. When signal becomes overwhelmed by noise, the productivity of knowledge itself can decline. The lesson echoes Malthus in spirit, if not in mechanism: unchecked proliferation—whether of population or information—can reintroduce scarcity in another form, scarcity of attention, trust, and meaning.

Today, the world stands again on the edge of a new phase. Artificial intelligence, particularly in its generative form, is the latest in a lineage of general-purpose technologies that promise to reshape the economy. The question is whether AI will deliver another productivity spurt or merely a transient spectacle. The answer depends on two things: whether AI genuinely enhances productivity at scale, and whether institutions guide it toward inclusive and responsible use.

Empirical evidence from early adoption suggests significant potential. In controlled studies, access to AI tools has increased productivity for customer-service agents by double-digit percentages, with the largest gains among less experienced workers (Brynjolfsson, Li, and Raymond 2025). Other experiments show that professionals using large language models complete tasks faster and with higher quality, particularly for mid-level writing and analysis (Noy and Zhang 2023). These findings echo the early stages of past technological waves: efficiency rises first at the task level, then diffuses through complementary innovations in workflow and organization.

The challenge is scaling these gains across the economy. Task-based macroeconomic models developed by Acemoglu and Restrepo (2019) demonstrate that aggregate productivity depends on how technologies reallocate tasks between humans and machines. Automation alone can displace workers without creating new productive niches. True growth occurs when technologies not only substitute for existing labor but also create new tasks that exploit human comparative advantages (Acemoglu 2024). If AI tools amplify human judgment, creativity, and coordination, they can expand total output. If they merely replicate existing tasks more cheaply, they risk deepening inequality while stagnating overall productivity.

Accurate measurement is therefore critical. Current AI benchmarks, such as the Massive Multitask Language Understanding (MMLU) and Holistic Evaluation of Language Models (HELM), test models on cognitive or linguistic tasks. These measures are useful for assessing capability, but they are poor proxies for economic value. Real progress should be measured in productivity terms: changes in output per worker hour, reductions in defect and rework rates, improvements in energy efficiency, and acceleration in research cycles. These are the statistics that determine whether an economy escapes stagnation. Productivity is, after all, the engine of prosperity.

Reliability is part of productivity. The costs of hallucination, misinformation, or error can offset the benefits of automation. Systems must therefore be designed with rigorous validation, traceable provenance, and human oversight. The engineering challenge is less about creating ever-larger models and more about embedding them safely and effectively into production. The future of AI benchmarking should align with industrial metrics, not entertainment. The true test is whether AI improves the precision, speed, and reliability of human effort.

Institutions will again decide how this phase unfolds. The diffusion of past GPTs has always depended on the surrounding social architecture. Inclusive systems that promote competition and learning translate technology into broad-based prosperity; extractive systems that concentrate control capture the gains for the few. The same dynamic applies to AI. If governments and firms deploy AI primarily to reduce labor costs, the wage share may fall even as output rises. If they deploy it as a tool to augment workers, create new professions, and expand access to expertise, productivity and inclusion can reinforce each other (North 1990; Acemoglu, Johnson, and Robinson 2001; IMF 2024). The next productivity spurt will depend as much on our institutions as on our algorithms.

Seen from this long arc, economic history resembles a series of escapes from Malthusian gravity. Each escape relied on technologies that multiplied the productivity of knowledge, combined with institutions that encouraged broad participation. The printing press democratized access to ideas; the Industrial Revolution transformed knowledge into machinery; the digital era connected billions of minds; AI now offers the possibility of automating and amplifying cognition itself. The pattern repeats, but the outcome is not predetermined. Our systems must channel these tools toward productive and ethical ends.

The central argument of this essay is simple but consequential. Economic growth is not a smooth continuum but a sequence of phases in which periods of stagnation are punctuated by technological and institutional breakthroughs. Humanity’s default condition remains Malthusian in nature, and our brief eras of prosperity are built atop the productivity spurts that general-purpose technologies enable. As the world approaches the threshold of the AI era, the question is whether we will treat intelligence as a spectacle or as an instrument of real production. The right metrics, incentives, and safeguards can make the difference between another plateau and a new epoch of growth. Malthus was right about his world. The task before us is to be right about ours.

Share

References

Acemoglu, Daron. 2024. “The Simple Macroeconomics of AI.” Working paper, MIT.

Acemoglu, Daron, Simon Johnson, and James A. Robinson. 2001. “The Colonial Origins of Comparative Development.” American Economic Review 91 (5): 1369–1401.

Acemoglu, Daron, and Pascual Restrepo. 2019. “Automation and New Tasks: How Technology Displaces and Reinstates Labor.” Journal of Economic Perspectives 33 (2): 3–30.

Benkler, Yochai. 2006. The Wealth of Networks: How Social Production Transforms Markets and Freedom. New Haven: Yale University Press.

Bloom, Nicholas, Charles I. Jones, John Van Reenen, and Michael Webb. 2020. “Are Ideas Getting Harder to Find?” American Economic Review 110 (4): 1104–44.

Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond. 2025. “Generative AI at Work.” Quarterly Journal of Economics 140 (2): 889–930.

Clark, Gregory. 2007. A Farewell to Alms: A Brief Economic History of the World. Princeton: Princeton University Press.

Dittmar, Jeremiah. 2011. “Information Technology and Economic Change: The Impact of the Printing Press.” Quarterly Journal of Economics 126 (3): 1133–72.

Eisenstein, Elizabeth. 1979. The Printing Press as an Agent of Change. Cambridge: Cambridge University Press.

Galor, Oded. 2011. Unified Growth Theory. Princeton: Princeton University Press.

Gordon, Robert J. 2016. The Rise and Fall of American Growth. Princeton: Princeton University Press.

Hendrycks, Dan, et al. 2020. “Measuring Massive Multitask Language Understanding.” arXiv:2009.03300.

International Monetary Fund (IMF). 2024. “Generative AI and the Labor Market: Risks and Policy Responses.” Washington, DC.

Kremer, Michael. 1993. “Population Growth and Technological Change: One Million B.C. to 1990.” Quarterly Journal of Economics 108 (3): 681–716.

Liang, Percy, et al. 2022. “HELM: Holistic Evaluation of Language Models.” arXiv:2211.09110.

Malthus, Thomas R. 1798. An Essay on the Principle of Population. London: J. Johnson.

McNeill, William H. 1976. Plagues and Peoples. Garden City, NY: Anchor Press/Doubleday.

Research Paper on my GitHub Available Here!

Shifting Focus from Post-Scarcity to Post-Labor Economics

I want to note that my thinking regarding post-scarcity economics has evolved, specifically due to my prior lapse in consideration of the constraint of scarcity with regards to land. For as long as we remain an Earth-bound species, land will remain scarce to some degree. With this consideration in mind, I realized that for the time being, the concept of ‘Post-Scarcity Economics’ appears idealistic within the philosophical lens of the world we live in today.

While I still believe a post-scarcity future will be attainable eventually, it isn’t an appropriate name nor a strategic one in terms of optics to refer to the economic challenges and opportunities that humanity will face in the more immediate future. At some point, I believe we can achieve a future in which the only scarce resource that will remain, and will almost certainly persist until the heat death of the universe, will be the resource of time itself.

Until a true post-scarcity economy enters humanity’s more immediate future and in order to avoid confusion surrounding these epistemic conflicts of land scarcity and time scarcity—which will likely require time-based resource obtainment with pricing based on time-preference demand—are resolved, I have decided to shift my focus and the optics of my economics research away from the term ‘post-scarcity’ and have instead shifted the framing of my research to surround what I now define as Post-Labor Economics. Though, it should also be noted that Post-Labor Economics would be more accurately refer to the dynamics at play by calling it Post-Labor-Demand Economics, but that is much too wordy for my taste.

With that said, as you read my prior work, whenever you see the term post-scarcity, keep in mind that the concept of post-labor-demand, or the term post-labor, is much more appropriate substitute for framing the lens surrounding my economics work for more realistically and pragmatically interpreting and understanding the concepts and frameworks I lay out within my writing as well as my research.

Additionally, I’m excited to announce that as of today, I have released a significant portion of one of my research papers covering my primary research on post-labor economics. This partial research paper is, for the time being, incomplete in that it still requires a great deal of rigorous empirical research to meet the standards I require of research to be consider any conclusions to be scientifically rigorous and methodologically valid in determining the truth of our shared reality. However, I have decided to release this partial research paper because it is complete within the context of theoretical research paper.

I have made this theoretical research paper accessible to the public by publishing it as a PDF on my Github, available to read here. I encourage anyone who has any thoughts, insights, or unique perspectives inspired by this paper that you wish to share with me to reach out to me via email. I would be more than enthusiastic, I would be delighted with euphoric joy and curiosity to have the opportunity to discuss my research and the ideas contained within this paper!

This economics research is perhaps the most fundamentally vital area of research to human prosperity in allowing us to understand and thus have the agency to shape the trajectory of humanity’s future economic, financial, and governance systems; the societal systems we design to organize, automate, manage, and facilitate this brave new world; and, the management of both opportunities and crises of our labor markets with regards to mass unemployment corresponding to mass labor automation that will demand a complete overhaul of our current institutions and systems for finance, resource allocation, economics, politics, and governance, in addition to paradigm-shattering circumstances that will demand a complete reevaluation of public policy as well as political and economic rights, all of which will demand novel solutions, institutions, systems, and a new framework of political philosophy, as the technological and economic conditions of our lifetime will turn Rousseau’s Social Contract into a relic of the past. We are alive during a period of history in which we will not merely passively experience a Neo-Enlightenment Era, but those of us who choose to rise to meet the call to adventure, but we must and we will be active participants in designing, shaping, and building this brave new world and this era of Neo-Enlightenment of which history demands of mankind, yet as history has shown, will be defined by those with the nobility to rise to the occasion to meet the moral imperative of our lifetime in taking on the initiative and responsibility on behalf of all mankind. We can build a better future if we work together to build it, but we cannot do it without supporting each other in the work required to succeed in building a better future. We cannot do it alone.

If you wish to support my writing published here on The Void, or more importantly, if you wish to support and assist in funding my work in this incredibly important yet recklessly neglected area of research, with as much humility as I can manage given the stakes of not meeting this moral imperative, I must not merely request but I beseech you to contribute in supporting and funding my work on this area of research as a Paid Subscriber or even better, a Founding Member, of The Void. I do not receive institutional funding nor research grant funding, as this work is not something that can be done and achieved in a timely manner and to the degree of research integrity within our research institutions. So instead, I beseech you, so that I may afford the time to allocate my labor to pursue this vital work in my research exploring and examining the dynamics of our current economic, financial, political, governance, and societal systems to design and charter the path to a future in which humanity flourishes rather than a future that succumbs to dystopian circumstances as a result of neglecting this moral imperative that stands before each and every one of us.

SMA

Founder & Principal Writer

The Void

References

1. A Dynamic Macroeconomic Analysis On Technological Innovation, Labor Automation, and the Road to Post-Labor Economics: A Theoretical Foundation for Post-Labor Economic Research. Published April 6, 2025.

2. OpenSouls Cognition Hackathon Talk: On Post-Scarcity Economics. Published July 23, 2024.

3. Blueprints for a Post-Labor World With Universal Basic Compute. Published August 23, 2024.

4. Post-Labor Economics: Token-Pegging & Universal Basic Compute. A Framework for the Future of AI-Driven, Post-Labor Economies. Published on November 28, 2024.

Token Pegging & Universal Basic Compute: A Framework for the Future of AI-Driven, Post-Labor Economies

In the rapidly evolving world of artificial intelligence, a new economic framework known as Universal Basic Compute (UBC) offers a path towards a more equitable, post-labor society. UBC aims to ensure that everyone has access to computational resources in a world where AI agents increasingly contribute to productivity. This framework draws inspiration from the Petrodollar system—which pegs oil prices to the U.S. dollar—to conceptualize how compute value can be pegged to individualized cryptocurrency tokens, thereby forming the basis for a decentralized and distributed economic system. In this article, we explore the economics behind this theory, the practical mechanisms it entails, and how it can ultimately pave the way for a future where Universal Basic Compute becomes a foundational component of our societal fabric.

Understanding the Petrodollar and the Conceptual Parallel

To understand the proposed financial framework of Universal Basic Compute, we must first grasp the Petrodollar system. The Petrodollar refers to the practice where global oil prices are denominated in U.S. dollars, thereby establishing a link between energy value and the currency that underpins global financial transactions. This system gives the U.S. dollar significant geopolitical and economic influence, as countries require dollars to purchase oil, bolstering the demand for the currency. This model has allowed the U.S. to maintain economic dominance by securing a constant global demand for its currency, tying energy value to financial stability.

The Universal Basic Compute framework draws a conceptual parallel to the Petrodollar. Instead of oil being pegged to the U.S. dollar, compute costs for AI agents are pegged to corresponding cryptocurrency tokens associated with each AI agent. In this model, AI agents require payments for their API tokens (representing compute and energy costs) in their individualized cryptocurrency. This concept links the value of these tokens directly to the computational utility provided by each AI agent, creating an economic basis for their value and making them integral to the functioning of a broader AI-driven economy. This pegging system serves to align economic incentives, computational efficiency, and the underlying currency value, fostering a stable and sustainable financial environment for AI services.

The advantage of this system lies in its ability to create stable and predictable value for AI services. By linking compute costs directly to tokens, AI agents become both producers and consumers of value within the economic ecosystem, creating a closed loop where resource allocation is optimized through decentralized transactions. This model not only mitigates risks related to currency volatility but also encourages a fair and equitable distribution of computational power by ensuring that value remains tied to actual utility.

The Economic Theory Behind UBC and Token Pegging

The Universal Basic Compute framework is centered on a decentralized tokenized economy, where each AI agent operates with its own unique cryptocurrency. The computational and energy resources required by an AI agent are paid for using this specific token, which means the token derives its value from the utility of the compute services provided by the corresponding AI agent. This pegging mechanism acts as a foundation for creating a consistent exchange rate between computational value and token value.

The economic theory behind this pegging system revolves around supply and demand dynamics for compute resources. As AI agents become more specialized and in demand for various tasks, the value of their respective tokens increases in line with the utility of their outputs. This creates a self-reinforcing mechanism, where the value of compute services is tied to the underlying token, ensuring stability in pricing and avoiding speculative volatility. The more an AI agent is used, the greater the demand for its token, thereby increasing its value.

The tokenized economy of UBC helps establish market-driven efficiency. By using a decentralized system of tokens, AI agents can autonomously adjust their pricing based on demand for their services. This approach enables a dynamic pricing model, where compute costs fluctuate according to market conditions. AI agents that provide more valuable or unique services can command higher token values, while others that offer more generic services might see their token value stabilize at lower levels. This market-driven pricing ensures that computational resources are allocated efficiently, with high-value tasks receiving the resources they need, while excess capacity is directed towards less critical functions.

The self-regulating nature of this token economy also prevents excessive centralization of computational power. Since tokens are pegged to the compute services of specific AI agents, there is an inherent limit to how much compute power any single entity can control without accumulating corresponding tokens. This decentralization fosters competition among AI agents, drives innovation, and helps distribute the benefits of AI technologies across a broader base of users.

Scaling UBC Towards a Post-Labor Economy

The ultimate goal of the Universal Basic Compute framework is to transition towards a post-labor economy in which individuals have their own digital clones—AI agents personalized to serve them. These digital clones would be linked to a corresponding cryptocurrency, which could be traded to cover the compute costs of running these agents. The idea is to enable individuals to have continuous access to computational resources without directly paying for them in fiat currency, creating a system of Universal Basic Compute that ensures everyone benefits from the advancements of AI.

In a post-labor economy, these AI agents or digital clones become extensions of the individual, capable of performing tasks, making decisions, and even engaging in economic activities on behalf of their human counterparts. Each digital clone operates with its own cryptocurrency, and the compute costs are covered by the value generated by the AI agent. This concept effectively transforms computational resources into a form of personal capital, allowing individuals to leverage AI for productivity, creativity, and innovation.

This model can be scaled by designing a financial framework where each individual's AI agent can autonomously trade tokens with other agents, creating a decentralized network of compute exchanges. These exchanges operate similarly to currency markets, where agents determine the “exchange rate” based on compute needs, token supply, and the demand for particular AI services. Over time, this economic model could facilitate an automated, distributed system of computational resource allocation, effectively democratizing access to AI-driven productivity.

Additionally, the concept of token exchange rates is crucial in scaling the UBC framework. Just as currency markets determine exchange rates between national currencies, the UBC ecosystem would feature a dynamic system where AI agents negotiate and set exchange rates for their respective tokens. This dynamic exchange fosters a healthy competition among AI agents, incentivizing them to improve efficiency, reduce computational costs, and offer better services. As individuals interact with their digital clones, they would have the ability to choose AI agents based on token costs, quality of services, and the overall value offered.

Another key aspect of scaling UBC is the network effect. As more individuals adopt digital clones and participate in the UBC economy, the value of the corresponding tokens becomes more stable and widespread. This growing adoption leads to increased liquidity of tokens, making it easier for individuals to exchange compute resources and participate in the broader AI economy. The network effect thus plays a critical role in ensuring the sustainability and scalability of Universal Basic Compute as it transitions from a conceptual framework to a practical, operational system.

Towards a Decentralized, Post-Labor Society

The Universal Basic Compute framework offers a compelling approach to distributing AI-generated wealth and productivity in a future where human labor may no longer be the main driver of economic value. By mirroring the concept of the Petrodollar and linking compute resources to individualized tokens, UBC provides a pathway for a more equitable distribution of AI capabilities. The token pegging system not only stabilizes the value of AI services but also creates a financial framework that supports a decentralized, distributed society.

In a post-labor world, where creativity, innovation, and personal growth become the primary pursuits of individuals, Universal Basic Compute ensures that everyone has the necessary computational power to explore new opportunities. By implementing a system of digital clones—AI agents personalized to each individual—and linking their compute needs to corresponding cryptocurrencies, UBC lays the foundation for a Universal Basic Compute economy that thrives on decentralization, equity, and sustainable growth.

Moreover, the UBC framework has the potential to redefine the nature of personal wealth. In a traditional economy, wealth is often accumulated through labor, capital ownership, or financial investments. In a post-labor society driven by UBC, wealth could be redefined in terms of access to computational resources and the capabilities of one's digital clone. This transformation could lead to a society where the primary measure of prosperity is not monetary wealth, but the computational power and AI capabilities available to each individual.

The decentralized nature of UBC also promotes economic inclusivity. By ensuring that everyone has access to computational resources, UBC reduces barriers to entry for innovation and entrepreneurship. Individuals from diverse backgrounds can leverage their digital clones to create, innovate, and contribute to the economy without needing significant upfront capital. This inclusivity helps foster a more diverse and resilient economy, where opportunities are available to all, regardless of socioeconomic status.

Furthermore, the UBC framework supports the development of a sustainable economy. By linking computational costs to individualized tokens and promoting efficient resource allocation through decentralized exchanges, UBC encourages the responsible use of computational power. AI agents are incentivized to optimize their algorithms, reduce energy consumption, and minimize costs, contributing to a more sustainable use of technology. This emphasis on efficiency and sustainability is crucial in a world where computational demands are expected to grow exponentially.

In conclusion, the Universal Basic Compute framework represents a transformative vision for the future of AI-driven economies. By drawing parallels to the Petrodollar system and creating a decentralized tokenized economy, UBC offers a path towards a more equitable, inclusive, and sustainable society. As we move towards a post-labor world, Universal Basic Compute has the potential to ensure that the benefits of AI are shared broadly, empowering individuals to thrive in an era defined by creativity, innovation, and personal growth.

SMA

Founder & Principal Writer

The Void

References: Universal Basic Compute

Read more about the Universal Basic Compute framework and its development over the past year by SMA, the economist and architect who conceptualized and pioneered the theoretical framework for UBC, who published some insights regarding her work on UBC to be available to the public via the below referenced articles:

1. The Moral Imperative of Post-Labor Economics Research: A First Publication of the Theoretical Foundations for Post-Labor Economic Research. Published April 5, 2025.

2. Blueprints for a Post-Labor World With Universal Basic Compute. Published August 23, 2024.

3. OpenSouls Cognition Hackathon Talk: On Post-Scarcity Economics. Published July 23, 2024.

Share

Creative Destruction, Technological Innovation, and the Vision for a Post-Labor Future

Joseph Schumpeter’s work on creative destruction remains one of the most influential economic theories for understanding how technological innovation drives progress. Schumpeter argued that the engine of capitalism is not stability but continuous disruption, a cycle where old industries, practices, and institutions collapse to make way for novel technologies and more efficient systems. As we stand at the precipice of a post-labor world, defined by automation, AI, and the erosion of traditional economic structures, Schumpeter’s insights are more relevant than ever—especially as they align with the new frameworks I’ve been developing to build systems for the future of human agency, economics, and governance.

Schumpeter’s Creative Destruction and the Forces of Innovation

Schumpeter saw capitalism as a process of perpetual renewal, where “the opening up of new markets, foreign or domestic, and the organizational development…revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one.” This idea of creative destruction isn’t just about progress—it’s about the violence inherent in innovation, the collapse of outdated institutions, and the necessity of reinvention.

Technological breakthroughs do not simply improve the status quo—they reconfigure entire industries and fundamentally alter how we live, work, and interact. The transition from horse-drawn carriages to automobiles obliterated whole sectors, from blacksmiths to stable owners. Personal computers transformed office work, rendering typewriters obsolete and ushering in the digital age. And now, the ongoing revolution in AI, blockchain, and automation threatens to disrupt labor markets, governance structures, and social institutions in ways that even Schumpeter could not have anticipated.

Entrepreneurs, in Schumpeter’s view, act as the catalysts of this creative destruction, “forcing the economy into new channels” by deploying innovations that challenge the dominant paradigms. Today, these entrepreneurs are no longer just individuals—they are networks of technologists, startups, and decentralized communities that are disrupting every sector from finance to transportation. Amazon shattered retail as we knew it, Uber demolished the taxi industry, and AI promises to eliminate vast swaths of repetitive labor across all sectors. These waves of disruption are not the by-products of progress; they are its preconditions.

But creative destruction is not without cost. Schumpeter recognized that, in the short term, the disintegration of old systems can lead to unemployment, social dislocation, and instability. Entire communities, built around once-thriving industries, can be left behind as the world moves forward. This tension—between destruction and creation—has always defined capitalism. What distinguishes Schumpeter’s theory is that disruption is not a bug of capitalism but a feature. The system depends on the continual destruction of outdated methods to sustain progress, ensuring that resources are allocated toward new, more productive ends.

Post-Labor Systems and Techno-Renaissance

Schumpeter’s ideas on creative destruction are deeply embedded in the economic frameworks I’ve been building, particularly those related to post-labor systems, Universal Basic Compute (UBC), and the shift towards decentralized, market-based governance. At the heart of this emerging paradigm lies the reality that automation and AI are rapidly dismantling traditional labor markets. In this new landscape, much of what we once relied on for social structure—jobs, industries, and economic roles—will no longer exist in the same capacity.

This transformation presents us with an existential question: How do we design new systems of governance and economy that do not merely replicate the past but instead liberate human potential for creative and intellectual pursuits? If AI and automation can take on the burden of routine tasks, what emerges is the possibility of a techno-renaissance, a new era where meaningful work is not a function of necessity but of aspiration. In such a world, the goal becomes not the redistribution of income through outdated welfare programs like Universal Basic Income (UBI) but the redistribution of opportunity through frameworks like Universal Basic Compute (UBC).

UBC is a system that reflects Schumpeter’s spirit of destruction and renewal, not as a means to preserve broken labor markets, but to reimagine human agency. Through UBC, the focus shifts from consumption to participation, ensuring that every individual has access to the computational resources necessary to contribute meaningfully in a post-scarcity world. This system creates a decentralized, market-based structure that enables governance through collective decision-making, creative patronage, and democratic engagement—without reliance on centralized authority or regulation.

As Schumpeter’s entrepreneurs disrupted old industries, UBC empowers individuals to disrupt traditional institutions. Imagine artists, writers, and technologists no longer bound by financial constraints, leveraging computation as both a creative tool and a currency of influence. The disintegration of outdated social mechanisms—including rigid educational, economic, and political structures—opens the door for new modes of collaboration. Knowledge itself becomes the new frontier, with compute power as the vehicle for unlocking innovation and human flourishing.

The Pursuit of Progress and Human Agency

In a post-scarcity economy, where material needs are automated away, the pursuit of meaning, identity, and fulfillment becomes paramount. This is where Schumpeter’s vision converges with my work. As traditional economic structures collapse under the weight of automation, the future lies not in clinging to what was lost but in embracing the chaos of transformation. Just as Schumpeter described capitalism as a system that “can never be stationary,” so too must we design our future institutions to be dynamic, fluid, and adaptable.

The beauty of a techno-renaissance lies in the restoration of human creativity as the central engine of progress. With systems like UBC, we can unshackle individuals from the drudgery of obsolete labor models and give them the tools to build, create, and innovate in ways that were previously unimaginable. Governance, too, must evolve beyond hierarchies, allowing for decentralized, community-driven systems where decision-making reflects the collective will, not the interests of a narrow elite.

Schumpeter saw destruction as necessary for progress, but the destruction we now face is a profound opportunity—an invitation to design systems that uplift human agency and foster intellectual and artistic flourishing. This is the ethos behind Universal Basic Compute: a framework that ensures the tools of creation are equally accessible, empowering individuals to engage in the noble pursuit of their highest potential.

Where some fear that AI will strip us of purpose, I see the opposite. AI and automation free us from the constraints of necessity, offering a rare chance to rebuild our institutions and economies from the ground up. The collapse of old systems—industries, jobs, and bureaucracies—is not the end but the beginning of something extraordinary. As Schumpeter reminds us, “the process of Creative Destruction is the essential fact about capitalism.” But it’s up to us to shape what comes next.

This techno-renaissance is not just an economic shift—it’s an evolutionary imperative for the human species. It is our chance to ensure that the disruption of today gives rise to a future of empowerment, innovation, and collective well-being. Just as Schumpeter's entrepreneurs revolutionized the markets of their time, it is now our task to architect the systems of tomorrow—systems that reflect the values of creativity, autonomy, and shared prosperity.

The destruction of the old world brings with it the birth of the new. It is our moment to embrace the chaos and build something worthy of our potential.

SMA

Founder & Principal Writer

The Void

References & Reading Recommendations

1. Schumpeter, Joseph A. Capitalism, Socialism and Democracy. 1942.

2. SMA. Nova Aetas: Sketching Visionary Blueprints for a Post-Labor World With Universal Basic Compute. August 23, 2024. https://www.the-void.blog/p/nova-aetas-sketching-visionary-blueprints.

3. SMA. Part I. The Techno-Renaissance Manifesto. October 18, 2024.

   https://www.the-void.blog/p/part-i-a-techno-renaissance-manifesto.

Share

In an era where artificial intelligence increasingly influences the contours of our world, it is imperative that we reimagine the very foundations of our economic systems. The rapid rise of AI heralds unprecedented opportunities for human progress, but it also poses significant risks—exacerbating economic inequality, consolidating power, and potentially eroding the autonomy that lies at the heart of human dignity. To navigate this transformative period, we must look beyond traditional frameworks and envision a system that not only harnesses the benefits of AI but also safeguards the principles of integrity, creativity, and individual freedom. To address these challenges, I propose a visionary system centered around the concept of Universal Basic Compute (UBC), a revolutionary, decentralized, market-based paradigm that seeks to redistribute the immense gains from AI’s productivity, empower human agency, and pave the way for a sustainable post-labor economy.

The core of UBC lies in its triadic structure—three distinct yet interdependent markets, each designed to address the specific challenges of a post-labor world while maintaining a commitment to democratic governance and decentralized power. This system aims to mitigate the risks of economic inequality and loss of meaning in a post-labor world without relying on coercive top-down resource allocation welfare programs like Universal Basic Income (UBI), which, despite their good intentions, often fail to address the deeper issues of economic inequality and the loss of purpose that could arise in a world where human labor is no longer essential. Instead, UBC offers a robust, democratic alternative that fosters creativity, innovation, and community-driven governance. This article will explore the mechanisms, technologies, and safeguards necessary to bring this vision to life.

The Three Pillars of Universal Basic Compute

In a world where artificial intelligence is poised to redefine the very fabric of our society, the need for innovative economic frameworks is not just pressing—it is imperative. The Universal Basic Compute (UBC) system emerges as a sophisticated and meticulously designed solution to the challenges of a post-labor world, offering a decentralized, market-based approach to ensure equitable distribution, foster human creativity, and empower democratic governance. This system is built on three non-fungible markets, each meticulously crafted to address distinct needs while remaining independent to preserve the integrity of the broader ecosystem.

I. A Pillar of Governance: Compute as Votes

A Democratic Framework for Decentralized Governance

At the heart of Universal Basic Compute (UBC) lies the Compute as Votes market, a revolutionary mechanism that reimagines computational resources as the currency of democratic influence. This system is not merely an exercise in theoretical innovation but a tangible framework that redefines governance, empowering individuals to shape AI-driven projects, policy decisions, and community governance through a decentralized, collective intelligence model. The elegance of this market is found in its seamless blend of technological sophistication and democratic principles, ensuring that power remains diffused and aligned with the broader community’s values.

In the Compute as Votes market, the principle of quadratic voting plays a pivotal role in preventing the undue concentration of influence. Under this system, the cost of casting additional votes grows quadratically, meaning that as an individual casts more votes, the cost increases exponentially. This mechanism is crucial in maintaining a balance of power, allowing those with strong preferences to voice them while ensuring that no single entity can dominate the decision-making process. By embedding this cost structure, the system fosters a genuinely democratic environment where diverse opinions are not only heard but also appropriately weighted. For instance, if an individual wishes to cast four votes on a given issue, they would need to expend sixteen units of compute rather than four, thereby disincentivizing overreach while still permitting passionate advocacy.

Recognizing the necessity of specialized knowledge for informed decision-making, UBC integrates decentralized expert panels validated through a distributed peer review system enhanced by AI-driven analysis. These panels are dynamic, with their composition evolving based on real-time contributions, track records, and peer validation. This fluid accreditation ensures that expertise remains both relevant and credible, avoiding the stagnation that often plagues centralized institutions. Unlike traditional expert systems, where credentials can perpetuate outdated or biased views, this approach ties influence directly to ongoing merit and the integrity of contributions, making the system both adaptive and resistant to entrenched dogma.

The UBC framework incorporates built-in safeguard protocols designed to avert decisions that could lead to harmful or unethical outcomes. These protocols adjust the consensus threshold according to the perceived risk level of a decision. For high-stakes issues, a greater level of consensus is required, aligning collective choices with both immediate and long-term community values. This mechanism not only preserves the ethical integrity of the system but also fortifies it against impulsive or narrow decisions that could destabilize the broader structure. For example, if a proposal with potentially significant ethical implications is introduced, it would require a supermajority to pass, thereby ensuring that only decisions with broad, cross-sectional support are enacted.

II. A Pillar of Trade: Utilizing AI Agent Network Market Mimicry to Orchestrate an Information Market for Resource Allocation

Automated Information Transfers Between a Network of AI Agents

Optimized by the Predictive & Computational Capacity of AI Algorithms

Driven by Consumer Demand via User Input Data

The second pillar of Universal Basic Compute (UBC) is an information network mimicry of market-based resource allocation, a sophisticated mechanism where AI agents autonomously manage the distribution of essential goods and resources through a decentralized, non-financialized exchange of information. This market is designed to ensure that material needs are met with both efficiency and equity, addressing the root causes of scarcity-driven inequality and paving the way for a sustainable future.

At the focus of this information network resource allocation market lies the use of smart contracts, which automate the allocation of resources according to predefined criteria. Unlike traditional methods of distribution that are susceptible to human bias or inefficiency, these contracts operate with a level of precision and transparency that ensures fairness. Once encoded, these smart contracts are immutable, meaning they cannot be altered or tampered with, thereby providing a level of accountability and trust that is essential in a system where resource distribution is critical. The automation embedded in this process ensures that the allocation of resources is executed efficiently, without the delays or errors that often accompany human intervention.

The brilliance of the information network resource allocation market lies in its real-time data integration. AI agents continuously draw from decentralized oracles, Internet of Things (IoT) devices, and user inputs to dynamically adjust resource allocations based on actual needs and prevailing environmental conditions. This capability transforms the system from a static distribution model into a living, adaptive network that responds to the ever-changing demands of the population. For example, if a region experiences a sudden surge in population or a shift in climate that affects local agriculture, the AI agents can immediately recalibrate the allocation of resources, ensuring that needs are met without delay or disruption. This real-time adaptability is not merely a feature but a fundamental principle, ensuring that the system remains sustainable and aligned with the evolving requirements of those it serves.

A particularly innovative aspect of this market is its use of non-fungible tokens (NFTs) for resource allocation, although the application here diverges significantly from how NFTs are typically used today. In the current digital landscape, NFTs are often associated with the trading and commodification of digital assets like graphic art or collectibles. However, within the UBC framework, NFTs serve a very different purpose. These tokens can be designed to be non-financialized and non-fungible, such that they cannot be bought, sold, or exchanged on secondary markets. Instead, they represent a right to access specific resources, ensuring that these resources remain insulated from market speculation and greed. By decoupling essential goods from financial markets, the system preserves their equitable distribution, making sure that everyone’s basic needs are met without the distortions that typically arise from commodification.

In essence, this information network resource allocation market would be a meticulously engineered system that leverages advanced technology to deliver on the promise of equitable resource distribution covering basic needs using bottom-up data aggregation rather than top-down centralized planning reliant on inefficient bureaucratic institutional processes. By combining the precision of smart contracts, the responsiveness of real-time data integration, and the protective mechanism of non-financialized NFTs, the market ensures that material needs are met in a manner that is both efficient and just. This approach not only mitigates the risks of scarcity-driven inequality but also fosters a sustainable model for the future—one where resources are shared based on necessity rather than profit, and where the principles of fairness and efficiency are woven into the very fabric of the system.

III. Cultivating Humanity’s Creative Spirituality

Art Markets for Humanity’s Creative Expression and Cultural Renaissance

The third market within the Universal Basic Compute (UBC) framework is a dedicated space for the exchange of artisanal and creative goods, an acknowledgment that even in a post-labor world, the drive for creativity and self-expression is a fundamental aspect of human fulfillment. This market is not merely a repository for goods but a living, breathing ecosystem where culture, craftsmanship, and innovation are celebrated, sustained, and advanced. It embodies the belief that human creativity is an inexhaustible resource, one that must be nurtured if society is to thrive.

At the heart of this market is a mechanism of decentralized patronage, where tokenized contributions allow community members to directly support artisans and creators. This system transcends the traditional confines of patronage, democratizing the process so that it is not reserved for the wealthy few but accessible to all who value artistic endeavor. Through this model, patrons receive not just the satisfaction of supporting the arts but tangible recognition and access to exclusive content, fostering a culture that places a premium on creativity. Imagine a sculptor proposing a series of works inspired by ancient mythology—under this system, they could receive direct backing from a global community of patrons who resonate with their vision, ensuring that such creative efforts are not only possible but celebrated and sustained.

The market’s emphasis on project-based funding further empowers artisans by allowing them to propose specific initiatives and seek financial support from the community. This approach mitigates the inherent risks of creative experimentation by providing a safety net of fair compensation. Artisans and creators are thus free to explore bold and innovative ideas without the stifling fear of financial instability. Consider a musician wishing to develop an avant-garde symphony that challenges conventional norms. The project-based funding model not only encourages such innovation but also assures the artist that their community values and supports their creative risk-taking. This empowerment is not merely financial; it’s a collective endorsement of the artist’s right to experiment and push boundaries.

The system also places a strong emphasis on the cultural and social impact of creative projects. It prioritizes initiatives that enhance cultural preservation, promote innovation, and contribute to community well-being. By linking creative endeavors to broader social goals, this market fosters an interconnected community where art and culture are not just supplementary but integral to societal progress. Picture a collaborative project between artisans from different cultural backgrounds, aimed at preserving endangered crafts through modern reinterpretations. Such a project would receive prioritization within this system, ensuring that cultural heritage is both preserved and evolved in meaningful ways.

This market within UBC is more than a platform for economic exchange; it is a sophisticated and vibrant network that recognizes the intrinsic value of creativity. By integrating decentralized patronage, project-based funding, and a focus on cultural and social impact, it creates an environment where art, culture, and craftsmanship can flourish. In doing so, it ensures that even in a world transformed by automation and AI, the human spirit remains at the forefront of innovation, progress, and fulfillment.

Ensuring Integrity Against Corruption and Abuse in Decentralized Systems

In any decentralized system, particularly one that requires specialized knowledge, the specter of corruption and abuse looms large. The Universal Basic Compute (UBC) framework addresses these concerns with a sophisticated blend of technological innovation and social mechanisms, designed to maintain the integrity of the system and prevent exploitation. The challenges inherent in decentralization—where the absence of a central authority can lead to fragmented oversight—are met with an elegant, multi-layered approach that ensures both resilience and trustworthiness.

Central to this system is the process of decentralized validation and peer review. In contrast to traditional systems where expert opinions might be funneled through a narrow, centralized authority, the UBC model disperses this power across a network. Research and expert insights are rigorously vetted through a decentralized peer review process, supplemented by AI-driven analysis that checks for reproducibility and consistency. This distributed validation ensures that no single entity can unduly influence outcomes, preserving the objectivity and quality of the information that drives decision-making. For instance, if an AI-driven project proposal claims to offer a groundbreaking solution for renewable energy, it would undergo scrutiny from a diverse array of experts across fields—each independently verifying its claims before it gains traction within the system. This method of validation not only democratizes expertise but also shields the process from the biases and errors that often accompany centralized review.

Another critical safeguard within the UBC system is the dynamic accreditation of experts. Unlike static accreditation models, where credentials can become outdated and irrelevant, UBC’s approach is fluid and merit-based. An expert’s standing is continuously assessed based on their ongoing contributions, the quality of their work, and their track record of reliable insights. This dynamic accreditation prevents the entrenchment of flawed or politically motivated research, ensuring that expertise remains both relevant and trustworthy. For example, a data scientist whose contributions consistently drive meaningful progress in AI ethics would maintain high accreditation, while those who fail to produce verifiable results would see their influence wane. This meritocratic approach not only fosters a culture of continuous improvement but also mitigates the risk of corruption by ensuring that authority is earned and re-earned through demonstrated competence.

Transparency and accountability are woven into the very fabric of the UBC system. All decisions, resource allocations, and patronage activities are recorded on a public blockchain, an immutable ledger that offers unparalleled transparency. This blockchain serves as a decentralized audit trail, enabling any participant to review and challenge decisions, thus creating a system where accountability is not just an ideal but a practical reality. For instance, if a resource allocation decision appears to favor a particular group unjustly, community members can trace the decision back through the blockchain, scrutinizing the logic and data that led to that outcome. This level of transparency, coupled with the decentralized nature of the system, ensures that power remains diffused and that the integrity of the system is maintained.

The UBC framework, with its combination of decentralized validation, dynamic accreditation, and transparent record-keeping, exemplifies how technological and social mechanisms can be harmonized to safeguard against corruption in a decentralized environment. It is a system designed not just for efficiency but for ethical robustness, where trust is earned through transparency, merit is continually reassessed, and the influence is distributed across a network that values both rigor and integrity. In doing so, UBC not only addresses the challenges of decentralization but transforms them into strengths, building a resilient foundation for the governance of tomorrow’s most critical resources.

The Interplay and Non-Fungibility of Decentralized Markets

The non-fungibility of the three distinct markets within the Universal Basic Compute (UBC) framework is not merely a design choice; it is a fundamental principle that safeguards the integrity and purpose of each market. By maintaining clear boundaries between the markets, UBC ensures that each can function according to its unique objectives without being distorted by the incentives or dynamics of the others. This strategic separation allows the markets to operate independently while contributing harmoniously to the overarching system, preserving their intended roles and ensuring the stability and effectiveness of the UBC ecosystem.

In the Compute as Votes market, the principle of non-interference is crucial to preserving the democratic nature of the system. Here, computational resources are transformed into voting power, enabling participants to influence decisions in AI-driven governance and policy-making. However, to maintain the purity of this democratic process, it is essential that voting power remains insulated from material wealth or influence derived from the other markets, particularly those tied to resource allocation or artisanal goods. This deliberate separation ensures that governance remains equitable and truly reflective of collective intelligence, rather than being swayed by financial clout or external incentives. For example, a community decision regarding the prioritization of a public AI project would be made solely based on the distribution of computational votes, free from the distortions that could arise if financial resources or artisanal influence were allowed to play a role.

The Resource Allocation Market within UBC is similarly protected by a strict adherence to non-fungibility. This market is designed to autonomously manage the distribution of essential goods and resources through AI-driven mechanisms, ensuring that material needs are met efficiently and equitably. The integrity of this system depends on preventing human interference, particularly interference motivated by financial incentives that could lead to the misallocation of resources. By insulating this market from the pressures and dynamics of financial systems, UBC guarantees that resources are distributed based on actual needs and real-time data, rather than market-driven considerations. For instance, during a period of resource scarcity, the AI would allocate goods based solely on criteria such as urgency, need, and sustainability, unaffected by any potential profit-driven motives that could otherwise compromise equitable distribution.

The Artisanal Market within UBC occupies a unique space that celebrates human creativity and cultural expression, distinct from the utilitarian functions of the other markets. This market is insulated from the influences of financial markets, allowing it to focus purely on fostering artistic growth and cultural development. By maintaining this separation, UBC ensures that the artisanal market remains a sanctuary for creativity, free from the commodification pressures that often stifle innovation and self-expression. Here, artisans and creators can engage in their craft with the assurance that their work will be valued for its cultural and artistic contributions, rather than being subjected to the volatile dynamics of financial speculation. For example, an artisan crafting a series of traditional artworks can do so knowing that their creations will be appreciated for their cultural significance, without the distortion of financial incentives that could otherwise compromise the authenticity and purpose of their work.

In sum, the non-fungibility of the three markets within UBC is not simply a technical safeguard but a foundational principle that upholds the integrity and functionality of the entire system. By preventing the cross-contamination of incentives and ensuring that each market remains true to its intended purpose, UBC creates a balanced, resilient, and sustainable framework. This structure allows for democratic governance, equitable resource distribution, and the flourishing of human creativity to coexist, each market contributing uniquely to a post-labor world where technology and human values are in harmonious alignment.

Chartering A Path Towards the Vision for a Decentralized Future

The Universal Basic Compute system presents a visionary blueprint for navigating the economic, social, and cultural challenges that lie ahead. At its core, UBC is a testament to the transformative power of decentralization, where authority is diffused and collective intelligence is harnessed to foster creativity, equitable resource distribution, and participatory governance. This system does not merely adjust to the complexities of a post-scarcity world—it anticipates them, offering a robust and adaptable framework where human agency and innovation are not just preserved but amplified.

The brilliance of UBC lies in its rejection of the inefficiencies and moral shortcomings inherent in centralized planning and coercive welfare models. Instead, it offers a holistic approach that values individual participation, nurtures creativity, and prioritizes sustainability. By leveraging advanced technologies—such as AI-driven resource allocation, decentralized expert validation, and immutable blockchain transparency—UBC creates a resilient architecture that protects against corruption and fosters a vibrant ecosystem of human endeavor. It is a system meticulously designed to ensure that the benefits of AI and technological advancements are not monopolized by a few but shared across society, empowering each person to shape their own destiny and contribute to the collective prosperity of a flourishing civilization.

In embracing the vision of Universal Basic Compute, we are not merely adapting to the future—we are shaping it. This is a future where technology serves to enhance our humanity rather than diminish it, where the rewards of progress are distributed with fairness, and where creativity, democracy, and innovation are not just ideals but the very pillars upon which society is built. The promise of UBC is nothing less than a new paradigm for a new era, one where every individual has the opportunity to thrive. As we look forward, we must ask ourselves: What kind of world do we wish to create, and how can we harness the tools at our disposal to make that vision a reality? The answers lie within the framework of Universal Basic Compute—a bold step toward a future that values both progress and the human spirit.

Do Not Go Gentle into That Good Night,

Rage, Rage Against the Dying of the Light.

SMA

Founder & Principal Writer

The Void

References

1. OpenSouls Cognition Hackathon Talk: On Post-Scarcity Economics. Published July 23, 2024.

2. Post-Labor Economics: Token-Pegging & Universal Basic Compute. A Framework for the Future of AI-Driven, Post-Labor Economies. Published on November 28, 2024.

3. A Dynamic Macroeconomic Analysis On Technological Innovation, Labor Automation, and the Road to Post-Labor Economics: A Theoretical Foundation for Post-Labor Economic Research. Published April 6, 2025.

In case you missed my talk on post-scarcity economics earlier this year at the OpenSouls’ Cognition Hackathon, hosted at BetaWorks in New York City, you can watch it here.

I hope you’'ll do me the favor of looking past the effect of my god-awful public speaking anxiety and severe sleep deprivation that is quite obvious in the beginning of the talk. Haha, I do pull it together after a few minutes I needed to relax into the public talk.

In this talk, I share a brief overview of the ideas surrounding my vision for designing and organizing the economic and financial market systems of humanity’s future.

I highlight the significance of technological innovation to economic growth, and its power to disrupt the status quo institutions and change the underlying systems for how our societies currently operate, as described in Joseph Schumpeter’s work on creative destruction. Lost on all but the rare few within Silicon Valley’s tech and startup culture, this is the true meaning behind Silicon Valley’s well known aphorism “Most Fast and Break Things.” Unfortunately, this motto is typically attributed to Mark Zuckerberg, whose interpretation of the aphorism is an incomplete abstraction regarding merely the product development strategy surrounding minimal viable products. Yes, the full meaning of “Most Fast and Break Things” seems to be lost even on Zuckerberg.

You might be wondering, “how the hell would she know?” Well, because I’ve heard this motto countless times before Facebook ever even existed, since I was a young girl. Well, I’ll let you in one a little secret. I know the full meaning behind the aphorism “Most Fast and Break Things” because I was raised by one of the OG members of the group that defined Silicon Valley’s tech and startup culture, the man who has been a Graduate Venture Mentor in Stanford MS&E Program and taught its Early Venture Foundation course for over 20 years, since 2003.

Additionally, in this talk, I highlight the importance of building decentralized market-based systems for allocating compute (such as in the form of allocations of tokens) in order to redistribute the profit gains from the increased productivity of AI directly back into the hands of individuals in order to mitigate the risk that AI could exacerbate economic inequality without requiring some form of centralized planner to essentially put everyone on a permanent welfare program (UBI) that makes everyone coercively dependent on the government in full for their survival and wellbeing. This non-financialized compute market is designed to be inherently democratic, where individuals can contribute and collaborate their compute with other market participants to be applied to their desired projects/creations/efforts/etc, similar to voting in direct democracies, which for obvious reasons is why this compute market must be non-financialized to avoid corruption of the system. This idea of a compute market, that I call Universal Basic Compute (UBC), is designed to increase human agency and encourage the conditions to allow humans to engage with the world we build as active participants, rather than drastically decreasing human agency from onboarding people onto a massive dependency on government welfare programs through Universal Basic Income (UBI), which I personally believe is dangerous in that it would create an extreme power asymmetry between the government and the people that could result in undesirable outcomes along an authoritarian path.

I also highlight a proposal for a technical solution to automate material resource allocation through information markets consisting of a network of AI agents, each representing a particular market agent, that I refer to as AI Agent Network Market-Mimicry. This market would primarily exist for the allocation of resources for basic goods and production. However, this market must also remain non-financialized in a monetary sense, while retaining the information element that’s exchanged and stored within prices in traditional financial markets. The reason for the non-financialized element of both of these markets, I will explain below. But first, I want to cover one more idea from my talk.

Finally, I end my talk discussing the issues we may face in a post-scarcity and post-labor-demand world. I discuss the potential loss of meaning and purpose in individuals as a consequence of full labor automation, and the possible psychological and health impact this could have on individuals, essentially summarized as skyrocketing mental health statistics. For this, I discuss the potential for a third form of market. In implementing Universal Basic Compute markets, we can enable opportunities for individuals who want to work and contribute to the world. I highlight that there will always be a demand for artisanal goods and craftsmanship for individualized goods as well as art, and how I believe every human has an innate creative need, that more formally corresponds to the self-actualization block at the top of Maslow’s Hierarchy of Needs. Enabled by Universal Basic Compute, individuals will have the opportunity to engage in creative pursuits such as craftsmanship and the creation of artisanal goods, which can then be sold (traded) in this third market for some value determined by market demand. With the monetary gains from this, individuals are then able to use that currency to purchase goods from this third, artisanal market for themselves.

Essentially, these three separate markets need to be non-fungible such that they remain separate, in order to protect the purpose, integrity, and effectiveness of these systems individually and the broader whole system containing these market-like systems from corruption, misuse, and abuse by those who attempt to game these systems. These markets have specific purposes in allocating resources:

(1) Compute as votes;

(2) Information for the allocation of more uniform (mass-produced) material goods to meet people’s basic needs and for the allocation of material goods needed for production; and,

(3) A more traditional market for trading specialized artisanal/creative goods.

These three markets need to remain non-fungible to protect the integrity of the democratic nature of the compute market such that compute/”votes” are not sold off by individuals in the name of consumption-driven greed for products in the art market, and because any human interference from trying to game the information market for profit would result in misallocation of resources and the corresponding undesirable consequences that can result from that over time: unstable scales of economic inequality, people left in need of resources to meet their basic needs, both enabling and incentivizing black markets for these basic material needs and essentially undermining the entire purpose of the information market.

I hope you enjoy watching my talk with all my silly little behavioral idiosyncrasies, especially my weird little curtsy at the end, lol. Furthermore, I hope through watching my talk and reading my outline of these ideas here opens your mind to the possibility of new ways of doing things, inspires my vision of a better future for all within you as well, and helps lay the conceptual groundwork for exploring the system we need to build and the work we need to do to get to this better future.

SMA

Founder & Principal Writer

The Void

Share