For two centuries, economic power has followed intelligence. The smartest individuals built the most valuable organizations. Consultants sold knowledge. Engineers designed systems. Analysts interpreted data. Universities credentialed the cognitively elite. The entire economic hierarchy emerged from the scarcity of cognitive labor.
Artificial general intelligence disrupts this pattern. Intelligence itself is becoming abundant. When a resource moves from scarcity to abundance, it ceases to be the primary source of power.
The question is not whether artificial intelligence will surpass human cognitive capabilities. The relevant question is economic: when intelligence floods the market as a commodity, where does structural power concentrate?
The Power Relocation Principle
A pattern repeats across major technological transitions.
When a resource becomes abundant, power shifts to the constraints that surround it.
When information became freely accessible through digital networks, attention became the scarce resource. When computing capacity became inexpensive, distribution channels became the primary value driver. When transportation infrastructure expanded globally, control of that infrastructure became dominant. When manufacturing achieved economies of scale, brand differentiation became the sustainable moat.
The constraint always determines the leverage point. Artificial general intelligence represents an unprecedented abundance of cognitive capacity. The strategic question becomes: where does power relocate?
Five Structural Chokepoints
Intelligence does not disappear as a factor in competitive dynamics. It simply ceases to be the binding constraint. Power relocates to the systems that surround intelligence and determine its deployment conditions.
Compute Infrastructure
Artificial intelligence models require computational substrate. At scale, this necessitates industrial data centers, energy contracts, and semiconductor fabrication capacity. Organizations that control compute infrastructure control the physical foundation of cognitive systems.
The market capitalization expansion of semiconductor manufacturers reflects their position as infrastructure providers in an economy where cognition is software. When intelligence becomes utility-grade, compute providers function as the equivalent of power grid operators.
Energy Systems
Large-scale artificial intelligence deployment is energy-intensive. Training frontier models consumes megawatts. Global inference operations could require gigawatts. Energy infrastructure is not subject to algorithmic optimization—it is constrained by physics and geography.
The entities and nations that secure reliable, cost-effective energy supplies will maintain structural advantages that model optimization cannot eliminate. Technology firms are acquiring nuclear generation capacity and executing long-term renewable energy contracts. These are strategic moves to secure the constraint beneath computational capacity.
Data Access and Pipelines
Model training requires data. Not generic web-scraped content, but proprietary, high-quality, continuously updated information streams. Entities controlling unique data possess structural leverage.
Enterprises maintain customer transaction histories. Governments hold regulatory filing systems. Research institutions generate experimental results. Platforms track behavioral patterns. Intelligence has become inexpensive to generate. Ground truth remains expensive to obtain.
Data access represents the new intellectual property regime. The challenge is not collection difficulty but institutional control. The systems that generate valuable data are protected by institutional frameworks, contractual arrangements, and network effects that artificial intelligence cannot replicate autonomously.
Distribution and Discovery Channels
When intelligence becomes abundant, product differentiation collapses. Every offering can incorporate sophisticated artificial intelligence. Every service can provide personalization. Every organization can deploy automated customer support.
Under these conditions, distribution becomes the critical variable. Platforms controlling discovery mechanisms—search engines, application marketplaces, procurement systems, content feeds—function as gatekeepers. These platforms determine visibility, market access, and consideration set inclusion.
Market leaders in artificial intelligence achieve dominance not through marginal technical superiority but through distribution advantages. Early platform access, user experience design, and developer mindshare create path dependencies that technical performance differences cannot overcome. Distribution becomes the final durable competitive moat.
Institutional Legitimacy and Regulation
Artificial general intelligence will not operate in unregulated markets. Deployment occurs within controlled environments where established institutions define operational parameters. Regulatory frameworks determine which artificial intelligence systems achieve market authorization.
Banking regulators determine which financial AI systems qualify as safe. Medical authorities decide which diagnostic models receive approval. Governments establish which AI systems may access citizen data. Intelligence can be abundant. Permission cannot.
Organizations that shape regulatory frameworks—or navigate them most efficiently—acquire structural advantages that model performance cannot offset. This dynamic applies across sectors and jurisdictions.
Strategic Implications
Most discourse on artificial intelligence focuses on model capabilities, benchmark performance, productivity gains, and automation potential. These factors matter but are becoming commoditized.
Frontier models converge toward similar performance on standardized tasks. Enterprises gain access to comparable AI agent capabilities. Knowledge workers adopt similar copilot assistance tools. Technical sophistication becomes table stakes rather than differentiation.
Long-term power does not concentrate in model quality. It concentrates in structural control—the infrastructure layers beneath intelligence that determine deployment scale, operational conditions, and benefit capture.
Enterprise Strategy
Organizations building artificial intelligence capabilities must recognize that intelligence itself represents necessary but insufficient differentiation. Sustainable competitive advantage derives from controlling proprietary data pipelines, owning distribution channels, navigating compliance frameworks, and securing infrastructure resources.
Enterprises treating artificial intelligence as productivity tooling miss the strategic imperative. Winners recognize AI deployment requires operating system transformation—building coordination infrastructure that executes faster than competitors, developing data systems that generate proprietary intelligence, and establishing distribution control that ensures market access.
Founder and Startup Dynamics
Startups differentiating on model quality face rapid replication. Differentiation based on "AI-powered" features provides no sustainable moat. Ventures that control deployment environments, proprietary data infrastructure, distribution access, or compliance pathways establish defensible positions.
The strategic question is not whether a venture uses artificial intelligence. The question is whether it controls the structural layers that determine how intelligence gets deployed and value gets captured.
National Competitiveness
Countries compete for artificial intelligence leadership. National competitive advantage will not be determined primarily by research excellence or the quality of domestic AI laboratories. Structural factors dominate: energy policy capacity to power AI infrastructure at scale, data governance frameworks enabling sovereign information pipelines, regulatory systems capable of rapid iteration without instability, and capital allocation toward compute infrastructure.
Nations treating artificial general intelligence as purely a research challenge will fall behind those recognizing it as a comprehensive economic restructuring that requires coordinated policy across energy, infrastructure, data, and regulatory domains.
Distributional Effects
The transition to abundant intelligence carries implications for economic distribution. If power concentrates in infrastructure systems, energy networks, data pipelines, and regulatory frameworks, wealth accumulates among entities controlling these structural assets—utilities, platforms, governments, and capital allocators.
The cognitive meritocracy—where educational attainment and intelligence translated to economic mobility—weakens. The emerging hierarchy privileges structural positioning over individual cognitive capability. Structural power proves more difficult to access than cognitive power. Educational achievement does not confer ownership of data pipelines or energy contracts.
This dynamic may increase inequality before stabilization occurs. The policy challenge extends beyond algorithmic fairness to questions of structural chokepoint governance: preventing permanent wealth extraction through infrastructure control requires different interventions than addressing productivity gains or job displacement.
The Structural Question
The fundamental economic question transitions from identifying the smartest individuals to determining who controls the systems surrounding intelligence.
This shift will determine organizational success trajectories, institutional stability, and economic power distribution over the coming decades. Most actors will continue optimizing for intelligence—developing superior models, recruiting talented teams, executing rapid experimentation. These activities remain necessary but insufficient.
Strategic advantage accrues to those recognizing that intelligence is becoming infrastructure. Infrastructure is controlled by entities owning the foundational layers beneath it.
Power does not disappear during technological transitions. It relocates. Understanding the destination of this relocation constitutes the central strategic challenge of the artificial intelligence era.