For the better part of a century, economists have operated with a relatively stable conception of what constitutes an open or free economy. While political systems have varied dramatically and regulatory approaches have diverged across nations and epochs, the conceptual foundations of economic openness have remained remarkably consistent. An open economy, in the classical formulation, is characterized by four interrelated freedoms: the ability of consumers to choose between domestic and foreign goods, the freedom of investors to allocate capital across borders, the autonomy of firms to select locations of production, and the mobility of labor across markets. These principles have underpinned globalization, shaped international trade agreements, and informed the post–Cold War economic order.
The rapid emergence and proliferation of artificial intelligence, however, compels a fundamental reconsideration of this framework. AI is not merely another productivity-enhancing technology that can be layered atop existing economic structures, analogous to previous waves of digitization or automation. Rather, it represents a systemic transformation—one that reorganizes capital flows, restructures labor markets, redefines comparative advantage, and introduces an entirely new class of infrastructural gatekeepers. Most critically, AI reintroduces scarcity into domains previously assumed to be characterized by abundance: compute capacity, energy resources, semiconductor supply chains, and the physical infrastructure required to train and deploy increasingly sophisticated models.
This essay advances the concept of the Free AI Economy and argues that we are witnessing a profound paradox. While AI appears to expand economic openness at the surface—through democratized access to powerful tools, global availability of applications, and reduced barriers to entry for certain activities—it simultaneously constrains openness at deeper structural levels. Control over foundational infrastructure becomes increasingly concentrated. The costs of participation rise dramatically. And the very definition of "free" access becomes contingent on capital subsidies that may prove unsustainable.
The central thesis of this work is that the sustainability, fairness, and genuine openness of the AI economy will not be determined by the proliferation of applications or measured by productivity gains alone. Instead, these outcomes will depend fundamentally on how AI infrastructure, capital flows, and geopolitical power are distributed, regulated, and governed. The question is not whether AI will transform economies—this transformation is already underway—but rather whether this transformation will deepen concentration and dependency or enable a new, more resilient form of economic openness.
Before examining how AI disrupts traditional economic structures, it is necessary to establish precisely what is meant by an open or free economy. In macroeconomic theory, openness has traditionally been conceptualized along four dimensions, each representing a distinct form of economic freedom.
The first dimension concerns the market for goods and services. In an open economy, consumers possess the ability to choose between domestically produced and foreign goods. This freedom is never absolute—tariffs, quotas, quality standards, and trade agreements all influence relative prices and availability—but the fundamental condition is the existence of meaningful choice. Consumers can substitute between domestic and international products based on quality, price, and preference. This freedom enables competition, encourages specialization according to comparative advantage, and generally promotes efficiency through the discipline of international markets.
The second dimension involves financial markets and capital allocation. An open economy permits investors to allocate capital across both domestic and foreign assets. While capital controls may exist—justified as mechanisms to prevent destabilizing flows, protect nascent industries, or maintain monetary policy autonomy—they are typically framed as temporary safeguards rather than permanent barriers. The underlying principle is that capital should flow toward its most productive uses, regardless of national boundaries, thereby optimizing global resource allocation and enabling risk diversification.
The third dimension concerns the autonomy of firms in determining where to locate production. In an open economy, companies retain the freedom to establish facilities, source inputs, and organize supply chains based on efficiency, regulation, labor availability, infrastructure quality, and market access. This freedom enables firms to optimize operations globally, responding dynamically to changes in costs, technology, and demand patterns. It also creates competitive pressure on governments to maintain attractive business environments.
The fourth dimension addresses labor mobility. Workers in an open economy possess some degree of freedom in choosing where to work, both domestically and internationally. While immigration laws, professional licensing requirements, and cultural factors constrain this freedom more severely than the movement of goods or capital, the principle remains: individuals should be able to seek employment opportunities that best match their skills and preferences, and firms should be able to access the talent they require.
Collectively, these four freedoms constitute the normative framework for economic openness. They enable specialization, competition, innovation, and efficiency. Deviations from these principles have historically required justification—national security concerns, infant industry protection, prevention of market failures, or protection of vulnerable populations. The legitimacy of economic systems has been closely tied to how well they preserve these freedoms while managing necessary constraints.
The emergence of AI fundamentally challenges the classical framework of economic openness because AI does not behave like previous general-purpose technologies. To understand why requires recognizing that AI is not a tool but an economic stack—a vertically integrated system of interdependent layers, each with distinct economics, different competitive dynamics, and varying degrees of concentration.
The AI stack can be conceptualized as comprising multiple layers, from the most visible to the most foundational:
At the application layer, users interact with AI-powered services: chatbots, coding assistants, image generators, analytical tools, and increasingly autonomous agents. This layer appears highly competitive and accessible, with new entrants regularly emerging and consumer-facing innovation proceeding rapidly.
Beneath this lies the model layer, where foundation models are developed, trained, and refined. This layer requires substantial capital, specialized expertise, and significant compute resources. While open-source initiatives exist, the frontier of capability remains concentrated among a relatively small number of organizations.
Supporting the model layer is the compute infrastructure layer, encompassing GPUs, CPUs, specialized accelerators, networking equipment, and the software orchestration systems that manage distributed training and inference at scale. This layer is highly concentrated, with semiconductor design and manufacturing representing significant barriers to entry.
Below compute infrastructure lies the data center layer: the physical facilities, cooling systems, power distribution, and networking that house computational equipment. Building and operating data centers at the scale required for frontier AI requires enormous capital investment and favorable geographical positioning.
At the base of the stack sits the energy layer: the power generation, transmission infrastructure, and cooling water access necessary to sustain operations. AI's energy demands are growing exponentially, making access to reliable, affordable, and increasingly low-carbon energy a fundamental constraint.
Finally, permeating all layers is the capital layer: the venture capital, corporate investment, and public funding that finances the entire stack. Capital flows determine which layers expand, which firms survive, and which technological approaches receive support.
This layered structure produces what can be termed inverted openness. The application layer—the most visible to consumers and policymakers—exhibits apparent openness: many providers, low switching costs, competitive pricing (often subsidized), and rapid innovation. However, as one moves down the stack toward foundational layers, concentration increases dramatically. The further from the user interface, the fewer the players and the higher the barriers to entry.
This inversion has profound implications. Openness at the application layer does not imply—and may actually mask—closure at the infrastructural level. Users may experience choice and competition while the firms serving them remain dependent on a small number of infrastructure providers. This dependency relationship fundamentally alters the nature of economic openness.
One of the most striking features of the contemporary AI economy is its divergence from traditional software economics. This divergence can be understood through what might be called the capital recycling loop—a mechanism that obscures true costs and creates synthetic margins through continuous capital infusion.
Consider a simplified but representative flow of value in the current AI economy. An end user pays a subscription fee—perhaps $20 per month—for access to an AI-powered productivity application. This application, however, incurs inference costs of $50 per month per user through calls to a foundation model API. The $30 gap is absorbed by venture capital invested in the application company.
The model provider, in turn, collects $50 in API revenue but spends $100 on cloud compute to serve that inference request. The $50 shortfall is covered by venture capital invested in the model company.
The cloud provider receives $100 in compute fees but invests $500 in data center infrastructure and GPU purchases to serve that demand. The $400 gap is financed through corporate capital allocation or public market investment.
The hardware manufacturer—the GPU supplier—receives $500 and generates substantial operating profit. Capital accumulates at the base of the stack.
This structure creates what can be termed synthetic margins. At the application and model layers, growth and adoption appear robust, but profitability is an artifact of capital subsidy rather than sustainable unit economics. The system is optimized for growth optics rather than economic sustainability.
This stands in stark contrast to traditional software economics, where marginal costs approach zero as scale increases. In classic software businesses, the cost of serving an additional user is negligible, enabling high gross margins and increasing returns to scale. AI systems, conversely, exhibit increasing marginal costs: serving more users requires proportionally more compute, which requires more infrastructure, which requires more energy.
Historical precedent illuminates the likely trajectory. When venture capital withdrew from ride-sharing platforms after years of subsidy-driven growth, prices surged dramatically. Rides that cost $2-3 during the growth phase now routinely cost $20-30 or more. The apparent affordability and openness of the service was an artifact of temporary capital allocation rather than sustainable economics.
The AI economy faces a similar dynamic. When capital shifts toward the next technological frontier—quantum computing, biotechnology, or some yet-unanticipated domain—the subsidies sustaining current AI pricing will diminish. Applications will need to achieve profitability. Model providers will need to charge costs that reflect true inference expenses. The apparent openness and accessibility of the AI economy may contract significantly.
In classical economics, the primary factors of production are land, labor, and capital. Information technology was supposed to transcend these physical constraints, creating an economy of bits rather than atoms, characterized by abundance rather than scarcity, and governed by network effects and increasing returns rather than diminishing ones.
AI reverses this progression. Despite its digital interface and its association with software and algorithms, AI is profoundly physical. It runs on silicon, copper, and rare earth elements. It consumes electricity measured in gigawatts. It requires cooling water, physical space, and favorable climates. It depends on supply chains spanning continents and subject to geopolitical risk.
Computational capacity has emerged as a binding constraint on AI development and deployment. The training of frontier models requires compute clusters representing billions of dollars in capital expenditure. Access to cutting-edge GPUs is rationed. Lead times for advanced semiconductors extend months or years. This scarcity creates structural power for those controlling compute resources.
Underlying compute scarcity is energy scarcity. AI data centers require reliable, affordable, and increasingly low-carbon electricity at unprecedented scale. The energy intensity of AI training and inference is growing faster than improvements in hardware efficiency. This makes proximity to energy generation, access to cooling resources, and favorable regulatory environments for power consumption critical determinants of competitive advantage.
These physical constraints mean that geography matters profoundly in the AI economy. Access to semiconductor manufacturing, proximity to energy resources, climate suitable for efficient cooling, political stability, and regulatory predictability all shape the geography of AI infrastructure. Nations and regions possessing these attributes gain structural advantages that cannot be easily replicated through software innovation or application-layer competition.
The concentration of infrastructure creates natural control points in the AI economy. Firms closest to foundational resources—GPU manufacturers, hyperscale cloud providers, and energy suppliers—occupy structurally dominant positions. They capture value regardless of which specific applications or models succeed. Their position at the base of the stack insulates them from competition at higher layers and creates path dependencies that are difficult to overcome.
The dynamics described above suggest a particular structure of winners and losers in the AI economy—one that may diverge significantly from initial expectations about which actors would capture value.
At present, GPU manufacturers occupy the most advantaged position in the AI stack. They sell physical products at high margins to customers who face structural demand and limited alternatives. Their position is insulated from model-level competition and application-layer volatility. Revenue is immediate and profits are real rather than projected.
Hyperscale cloud providers and major technology platforms occupy a powerful but more complex position. They possess existing data center infrastructure, established customer relationships, and integrated product ecosystems. However, they face the challenge of massive capital requirements to build AI-specific infrastructure while defending their positions against both upstart model providers and direct competition from hardware manufacturers moving up the stack.
Foundation model developers face a precarious position. They sit between demanding compute costs below and pricing pressure above. While they may achieve high valuations based on growth projections, translating this into sustainable profitability requires either achieving such dominant positions that switching costs protect margins, or compressing infrastructure costs through vertical integration or radical efficiency improvements.
Application-layer companies—the most visible and seemingly most competitive part of the AI economy—may paradoxically be the most vulnerable. They face inference costs that scale with usage, competition that drives down prices, and dependency on both model providers and infrastructure operators. Unless they can achieve sufficient differentiation or lock-in to command sustainable margins, many risk becoming merely distribution channels for infrastructure services.
An underappreciated category of winners may be energy providers positioned to serve AI infrastructure. As AI compute demands grow exponentially, access to reliable, affordable electricity becomes critical. Utilities, renewable energy developers, and nuclear power operators with capacity near favorable data center locations may capture significant value.
The scale, strategic importance, and concentration dynamics of AI infrastructure raise fundamental questions about the appropriate role of government intervention. Unlike previous digital technologies, which could largely be left to market forces with relatively light-touch regulation, AI infrastructure may be too capital-intensive, too geopolitically sensitive, and too economically consequential to be governed by markets alone.
Several arguments support government investment in AI infrastructure. First, market concentration at foundational layers may lead to monopolistic or oligopolistic outcomes that reduce innovation and increase costs. Public investment could provide competitive alternatives. Second, national security and economic sovereignty concerns may necessitate domestic AI capabilities independent of foreign control. Third, AI infrastructure exhibits characteristics of public goods or natural monopolies that may justify public provision or regulation.
However, government competition with the private sector in AI infrastructure presents significant risks. Public sector entities may lack the agility, technical expertise, and incentive structures necessary to operate efficiently at the technological frontier. Political pressures may lead to inefficient capital allocation. Bureaucratic processes may slow innovation. And government involvement may crowd out private investment rather than complementing it.
The most promising approach may be hybrid models that combine public and private capabilities. Governments could provide capital for foundational infrastructure while contracting with private operators for management. They could establish public-private partnerships that share risk and expertise. They could offer incentives for private infrastructure investments that align with national strategic goals. Or they could focus on ensuring competitive access to infrastructure through regulation rather than direct provision.
The challenges facing the AI economy are not entirely without precedent. Previous technological transitions—particularly the commercialization of the internet, the rise of cloud computing, and the proliferation of IoT—offer instructive parallels and lessons.
The early internet was initially dominated by a small number of technology firms and proprietary systems. Its transformation into a global platform enabling competition and innovation was critically dependent on the establishment of open standards. TCP/IP, HTTP, HTML, and related protocols created interoperability, reduced barriers to entry, and prevented any single entity from controlling the network.
Crucially, these standards were developed through multi-stakeholder processes involving firms, governments, academic institutions, and civil society. They were deliberately designed to be implementable by anyone, preventing lock-in and enabling competition. The lesson is clear: open, interoperable standards can counterbalance natural tendencies toward concentration.
Cloud computing followed a different trajectory. While open standards exist, the market quickly consolidated around a small number of hyperscale providers. Governments responded through data sovereignty laws requiring certain data to be stored domestically, interoperability requirements to reduce lock-in, and regional initiatives to develop alternatives to US and Chinese platforms.
The European Union's GAIA-X initiative exemplifies this approach: an attempt to create federated, secure data infrastructure that reduces dependency on foreign providers while maintaining commercial viability. While implementation has proven challenging, the strategic logic—preserving autonomy through public investment and regulatory frameworks—remains sound.
The Internet of Things offers a more cautionary tale. Despite numerous attempts at standardization, the IoT ecosystem remains highly fragmented. Proprietary protocols, incompatible devices, and walled gardens limit interoperability and network effects. This fragmentation has slowed adoption and reduced consumer welfare.
The lesson is that standards must be established early and adopted widely. Once ecosystems fragment and lock-in occurs, coordination becomes exponentially more difficult.
These historical cases suggest several principles for AI governance. First, open standards for model interoperability, data formats, and infrastructure interfaces should be prioritized early. Second, data sovereignty and strategic autonomy concerns justify regulatory intervention and public investment. Third, international coordination is essential but difficult, requiring sustained diplomatic effort. Fourth, timing matters: interventions are most effective before dominant positions solidify.
AI differs from previous general-purpose technologies in the directness and significance of its linkage to national power. This creates fundamental tensions between the economic benefits of openness and the strategic imperatives of sovereignty.
AI capabilities are increasingly central to military planning, weapons systems, intelligence analysis, and strategic decision-making. The nation that achieves superiority in military AI applications gains potentially decisive advantages. This creates powerful incentives to restrict access to frontier capabilities, limit technology transfer, and invest heavily regardless of short-term economic returns.
Beyond direct military applications, AI is becoming embedded in critical infrastructure: power grids, financial systems, healthcare networks, transportation systems, and communication infrastructure. Dependence on foreign AI systems for these functions creates vulnerabilities that adversaries might exploit. This concern drives efforts to develop domestic AI capabilities and reduce reliance on external providers.
AI applications in drug discovery, medical diagnostics, and potentially human enhancement raise both economic and ethical sovereignty concerns. Nations seek to ensure that critical healthcare capabilities are not subject to foreign control and that development occurs within their own ethical frameworks.
The strategic competition between the United States and China provides the clearest manifestation of AI's geopolitical significance. Both nations treat AI superiority as a national priority, invest massively in AI capabilities, and implement export controls and investment restrictions to limit the other's access to critical technologies. The semiconductor supply chain—particularly advanced chip manufacturing in Taiwan—has become a critical flashpoint in this competition.
Given these tensions, complete openness in AI is politically implausible. The most realistic scenario is layered openness: relatively open commercial ecosystems for consumer and business applications, combined with restricted access to frontier capabilities deemed strategically sensitive. The challenge lies in determining where to draw these boundaries and how to prevent commercial restrictions from unnecessarily stifling innovation.
For countries outside the United States and China—which dominate AI development through combinations of technical capabilities, capital availability, market size, and strategic commitment—competing effectively in the AI era requires deliberate, comprehensive strategy rather than reactive improvisation or simple imitation.
The first essential step is identifying where AI can generate the greatest value within the specific context of the national economy. This requires honest assessment of existing strengths, competitive advantages, and areas of strategic importance. Rather than attempting to compete across all AI applications, nations should focus on domains where AI amplifies existing capabilities or addresses critical national challenges.
For a nation with advanced manufacturing sectors, AI applications in industrial optimization, supply chain management, and quality control may offer the highest returns. For agricultural economies, precision agriculture and climate adaptation technologies may be most valuable. For nations with advanced healthcare systems, medical AI and drug discovery may warrant priority. The key is strategic focus rather than diffuse effort.
Clear, predictable regulatory frameworks for AI development and deployment are essential for attracting investment, enabling innovation, and maintaining public trust. These frameworks should address data protection and privacy, algorithmic transparency and accountability, liability for AI systems, and ethical guidelines for sensitive applications.
Importantly, regulation should be proportionate to risk and adaptive to evolving technology. Overly restrictive early-stage regulation can stifle innovation and deter investment. The goal should be establishing guardrails that enable responsible innovation rather than attempting to control every aspect of AI development.
Strategic investment in foundational capabilities is necessary for long-term competitiveness. This includes physical infrastructure—data centers, compute clusters, and energy capacity—as well as institutional infrastructure—research universities, national laboratories, and innovation hubs.
The scale of required investment likely exceeds what private markets will provide based on near-term returns, particularly for smaller economies. Public investment can de-risk foundational infrastructure, enabling private sector innovation to build upon it. Public-private partnerships can align incentives and combine public capital with private expertise.
No amount of infrastructure or capital can substitute for human talent. Comprehensive AI strategy requires substantial investment in education: reforming curricula to emphasize computational thinking and statistical reasoning, expanding computer science and AI programs at universities, creating pathways for mid-career professionals to acquire AI skills, and attracting international talent while retaining domestic experts.
Brain drain represents a critical vulnerability for emerging economies. Even substantial investments in education yield limited returns if talented individuals emigrate to higher-paying markets. Retention strategies—competitive compensation, research opportunities, quality of life, and connection to meaningful national challenges—are essential complements to education investments.
Vibrant domestic AI industries require supportive ecosystems: venture capital availability, startup accelerators and incubators, connections between research institutions and industry, reasonable paths from research to commercialization, and sufficient market size to enable scaling.
Government can catalyze these ecosystems through multiple channels: direct funding for promising startups, tax incentives for AI research and development, procurement policies favoring domestic AI providers for government services, and programs connecting researchers with entrepreneurs and investors.
Control over data—both access and governance—represents a critical source of leverage in the AI economy. Nations should establish clear policies regarding data localization for sensitive information, data rights and ownership, cross-border data flows, and government access to data held by private entities.
This does not necessitate complete data isolation, which would reduce economic efficiency and limit AI capabilities. Rather, it requires deliberate choices about which data must remain under national control for sovereignty or security reasons, and clear legal frameworks governing all data use.
International AI standards—covering model interoperability, safety testing, ethical guidelines, and technical specifications—will shape the structure of the global AI economy. Nations that actively participate in developing these standards can influence outcomes to align with their values and interests. Passive adoption of standards developed elsewhere forfeits this influence.
Participation in international AI governance forums, technical standardization bodies, and diplomatic initiatives allows smaller nations to amplify their influence through coalitions and technical expertise rather than competing solely on economic scale or military power.
While much discussion of national AI strategy focuses on domestic applications, export potential should not be neglected. AI capabilities developed to address national challenges may prove valuable to other nations facing similar contexts. Deliberately designing solutions for export—including appropriate documentation, training, and support—can generate economic returns and diplomatic influence.
This approach treats AI as an export industry rather than merely domestic infrastructure, creating additional incentives for investment and enabling economies of scale beyond domestic markets.
Examining specific national approaches to AI strategy illuminates how different countries balance competing priorities and adapt general principles to local contexts.
Japan has explicitly positioned AI as a response to demographic aging and labor force contraction. The national AI strategy emphasizes widespread adoption of AI tools across businesses and government, integration of AI into manufacturing and robotics, and regulatory frameworks that encourage experimentation while addressing privacy and ethical concerns. Japan has allocated substantial public funding to AI research and established guidelines for responsible business AI use. The strategy reflects Japan's existing strengths in manufacturing, robotics, and industrial organization while addressing pressing demographic and economic challenges.
Singapore's AI strategy leverages its advantages as a small, wealthy, technologically sophisticated city-state with strong governance capacity. The strategy involves multi-billion dollar investments in AI research institutes and innovation hubs, development of AI governance frameworks and ethical guidelines that balance innovation with safety, strategic focus on AI applications in finance, logistics, healthcare, and smart city technologies, and positioning as a neutral hub for international AI collaboration and standards development. Singapore's approach demonstrates how smaller economies can achieve influence through strategic focus, institutional quality, and governance innovation.
Germany and France have pursued AI strategies emphasizing European sovereignty, industrial application, and human-centric values. Germany has allocated over €1.6 billion to AI projects focusing on manufacturing, healthcare, and mobility. France's national AI plan includes substantial funding to support AI adoption across French businesses, regulatory frameworks emphasizing explainability and accountability, and collaboration with other European nations on data infrastructure projects like GAIA-X.
These strategies reflect European concerns about dependence on US and Chinese technology platforms, commitment to different regulatory approaches to privacy and algorithmic accountability, and attempts to leverage existing industrial strengths rather than competing directly in consumer internet applications.
Brazil's national AI strategy allocates approximately $4 billion toward AI infrastructure, research, and application development, with particular emphasis on using AI to improve public services—healthcare, education, public safety—and addressing challenges in agriculture, environmental management, and sustainable development. The strategy includes efforts to develop domestic AI talent through educational initiatives and investments in research institutions.
Brazil's approach illustrates challenges facing large emerging economies: significant domestic needs that AI could address, limited capital compared to developed nations, need to balance openness to foreign technology with development of domestic capabilities, and opportunities to develop specialized expertise relevant to other emerging economies.
These cases reveal several patterns. Successful national AI strategies align with existing strengths and pressing national challenges rather than attempting to imitate leaders. They combine public investment in foundational capabilities with support for private sector innovation. They balance openness to international collaboration with protection of strategic autonomy. And they treat AI as a multi-decade transformation requiring sustained commitment rather than a short-term technology trend.
Understanding successful approaches requires equal attention to common failures. Several characteristic mistakes undermine national AI strategies.
Complete reliance on foreign AI platforms, infrastructure, and expertise creates strategic vulnerability and prevents development of domestic capabilities. While no nation can achieve complete self-sufficiency in all AI domains, minimum viable independence in critical areas is essential for long-term sovereignty and security.
Implementing restrictive AI regulations before developing domestic AI capabilities often backfires. Rather than fostering responsible local innovation, it drives AI development to more permissive jurisdictions while providing no safety benefit, as domestic users simply access foreign AI services. Effective regulation requires domestic AI industries capable of compliance and participation in standards development.
Failing to invest in AI education and research, or investing without strategies to retain talent, wastes resources and strengthens competitors. Many nations train excellent AI researchers who then emigrate to better-funded opportunities abroad, effectively subsidizing foreign AI industries.
Attempting to directly replicate the AI strategies of leading nations without adapting to local context, resources, and comparative advantages typically fails. A nation's optimal AI strategy depends on its specific economic structure, existing capabilities, geopolitical position, and societal values. Successful adaptation requires understanding underlying principles rather than superficial imitation.
Spreading limited resources thinly across all AI domains rather than concentrating on areas of potential competitive advantage dissipates effort without achieving critical mass in any domain. Strategic focus and prioritization are essential, particularly for smaller economies.
Viewing AI exclusively as a productivity tool to be applied to existing industries, rather than recognizing it as a major new industry with its own supply chains, business models, and export potential, limits ambition and forfeits opportunities.
The framework of economic complexity—which measures the diversity and sophistication of an economy's productive capabilities—provides valuable insight into how AI might benefit nations currently dependent on relatively simple export bases.
Consider the case of Australia, a wealthy developed nation whose economic complexity measures surprisingly low, comparable to far less developed economies. This paradox reflects heavy dependence on commodity exports—particularly iron ore to China—with limited diversification into sophisticated manufactured goods or complex services. While this specialization has generated substantial wealth, it creates vulnerability to commodity price volatility and provides limited foundation for future growth as global demand patterns shift.
AI offers potential pathways to increased economic complexity for resource-dependent economies. By applying AI to existing industries—mining operations, agricultural production, logistics and supply chains—nations can move up value chains from raw material extraction toward sophisticated services and technologies. For example, developing world-leading AI systems for mining optimization, precision agriculture, or resource management could create exportable capabilities and reduce dependence on physical commodity exports.
The transformation would involve several stages. Initial AI adoption would improve efficiency and competitiveness in existing industries. Accumulated expertise would enable development of specialized AI tools and platforms applicable to similar industries globally. Export of these AI capabilities would diversify revenue sources and reduce commodity dependence. Over time, reputation and expertise in specific AI domains would attract talent, investment, and partnerships, creating self-reinforcing growth.
This transformation faces substantial obstacles. Resource industries may generate sufficient profits to reduce incentives for diversification. Smaller markets may limit ability to amortize AI development costs. Brain drain to larger technology hubs may prevent accumulation of critical expertise. And path dependencies in institutional structures, educational systems, and cultural expectations may resist change.
Success requires not just technical capabilities but institutional adaptations: venture capital ecosystems willing to fund technology startups, immigration policies attracting international talent, educational institutions producing AI expertise, and cultural shifts elevating technology entrepreneurship alongside traditional industries.
While Australia provides a specific example, the principle applies broadly. Nations dependent on agricultural exports, tourism, natural resources, or labor-intensive manufacturing can potentially use AI to increase economic complexity, move up value chains, and reduce vulnerability to external shocks. The key is identifying specific domains where existing knowledge and AI capabilities can combine to create valuable, exportable offerings.
Returning to the concept introduced at the outset, what precisely is meant by a Free AI Economy, and what are its implications for economic theory, policy, and practice?
The Free AI Economy can be defined as an economic system characterized by widespread access to AI applications and tools at the surface level, while control over foundational infrastructure, compute resources, and capital flows remains concentrated among a small number of dominant actors. It exhibits the appearance of openness—competitive application markets, low switching costs, rapid innovation—while fundamental dependencies and power asymmetries constrain actual freedom of choice.
This definition captures the paradox at the heart of current AI economics: the coexistence of apparent democratization with structural concentration.
The classical four freedoms defining economic openness must be reconceptualized in this context. Freedom to choose goods and services at the application layer may coexist with constraints on infrastructure access. Capital may flow freely to AI applications while infrastructure investment remains concentrated. Firms may appear to have production location freedom while depending on centralized compute resources. And labor mobility may increase for AI-skilled workers while becoming constrained for those displaced by AI automation.
Assessing economic openness in the AI era therefore requires looking beneath surface-level metrics to examine structural dependencies and infrastructure control.
Perhaps the most critical implication concerns sustainability. If the current Free AI Economy depends substantially on capital recycling and venture subsidy—as the analysis suggests—then its apparent openness may prove temporary. When capital subsidies diminish, prices will rise, marginal players will exit, and concentration will increase further. This trajectory would transform an economy that appears relatively open today into one significantly more closed tomorrow.
Preventing this outcome requires addressing the underlying economics: either finding ways to reduce AI infrastructure costs dramatically, accepting that AI services will be significantly more expensive than current subsidized pricing, or implementing policies that prevent excessive concentration and maintain competitive access to infrastructure.
For policymakers, the Free AI Economy framework suggests several priorities. First, infrastructure governance—ensuring competitive access, preventing anticompetitive behavior, and potentially providing public alternatives—matters more than application-layer regulation. Second, early intervention is critical; allowing excessive concentration to develop before attempting to promote competition faces much higher barriers. Third, international cooperation on standards, interoperability, and governance can expand the feasible set of solutions available to individual nations. Fourth, investment in domestic capabilities—infrastructure, talent, research—is essential for meaningful participation rather than dependent consumption.
For businesses, understanding the Free AI Economy structure suggests specific strategic priorities. Companies operating at the application layer should carefully assess their infrastructure dependencies and develop strategies to reduce strategic vulnerability, whether through multi-cloud approaches, investment in proprietary infrastructure, or vertical integration. Companies controlling infrastructure should recognize the policy and regulatory attention their position will attract and invest in demonstrating responsible stewardship. And all firms should carefully evaluate whether current AI economics reflect sustainable realities or temporary subsidy effects.
This essay has argued that the emergence of artificial intelligence represents not merely another technological innovation but a fundamental restructuring of economic systems. The Free AI Economy—characterized by apparent openness at the application layer and concentration at the infrastructure layer—presents both opportunities and risks.
The opportunities are substantial. AI capabilities could enable dramatic productivity improvements, accelerate scientific discovery, address pressing global challenges, and allow economies to increase their complexity and sophistication. The potential for AI to reshape economic structures in positive ways is real and significant.
However, realizing these opportunities while preserving meaningful economic openness requires deliberate action. Market forces alone appear likely to produce increasing concentration, particularly given the capital intensity of AI infrastructure, the physical constraints on compute and energy, and the strategic imperatives driving government intervention.
The question is not whether AI will transform economies—this transformation is already underway—but rather what form this transformation will take. Will the Free AI Economy evolve toward greater actual openness, with competitive infrastructure access, distributed capabilities, and preserved choice? Or will apparent surface-level openness gradually give way to deeper concentration, dependency, and constrained freedom?
The answer will be determined by choices made in the coming years: choices about infrastructure investment and governance, about standards and interoperability, about regulation and competition policy, about education and talent development, about international cooperation and strategic competition, and about the balance between market forces and public interest.
What is clear is that these choices cannot be deferred. The structure of the AI economy is being determined now, through investment decisions, policy choices, and competitive dynamics. Path dependencies are forming. Lock-in is occurring. The degree of difficulty for changing direction increases with each passing month.
For nations seeking to compete effectively in this environment, comprehensive strategy is essential. This strategy must address all layers of the AI stack, from applications to infrastructure to energy. It must balance openness with sovereignty, competition with cooperation, and short-term pragmatism with long-term positioning. It must be grounded in honest assessment of capabilities and limitations while maintaining ambition for what might be achieved.
For the global community, the imperative is to establish governance frameworks that preserve the benefits of AI innovation while preventing excessive concentration of power. This requires international cooperation that has thus far proven elusive, with AI increasingly positioned as a zero-sum competition between nations rather than a shared challenge requiring collective action.
The stakes could hardly be higher. AI will shape labor markets, determine competitive advantage, influence military balance, and affect the distribution of wealth and power within and across nations. How the Free AI Economy evolves will fundamentally shape the 21st century economy.
The vision of a genuinely open AI economy—one in which the freedoms that have traditionally defined economic openness are preserved and extended—remains achievable. But it will not emerge automatically from market forces. It will require sustained effort, strategic investment, intelligent regulation, international cooperation, and constant vigilance against concentration and capture.
The challenge is clear. The path forward is not. But the urgency of addressing these questions is undeniable. The Free AI Economy we are constructing today will shape economic life for decades to come. Ensuring that this economy is genuinely free—not merely in appearance but in substance—is among the defining challenges of our era.
@article{Asefi2026FreeAIEconomy,
title = {Free AI Economy},
author = {Houman Asefi},
year = {2026},
url = {https://houmanasefi.co/essays/free-ai-economy.html}
}