For decades, corporate power was measured by market dominance. Companies with the largest customer bases, deepest supply chains, and most capital commanded the competitive landscape. Yet, in today’s AI-driven economy, a new hierarchy is emerging. The real power lies not in controlling markets, but in controlling the models that interpret data, drive decision-making, and increasingly set the pace of industries.

In this new era, those who build, own, and govern AI models form the backbone of corporate influence. They are the new elite, shaping not just how companies operate, but how entire markets evolve.

From Market Power to Model Power

Traditional hierarchies were built on financial leverage and distribution reach. Corporations that mastered manufacturing scale or market expansion could maintain dominance for decades.

AI has disrupted this paradigm. The locus of value is shifting from market share to model share. Competitive advantage no longer rests solely on how many customers a company reaches, but on the intelligence and adaptability of the AI models guiding its operations.

In this sense, models have become a new form of corporate capital—one that is more dynamic and enduring than traditional assets.

Why Models Are the New Power Centers

Data as the New Oil—But Models as the Refineries

The saying “data is the new oil” is incomplete. Just as crude oil is useless without refineries, raw data holds little value without models to refine it into actionable insight. AI models transform massive, unstructured datasets into intelligence that drives strategy, automates operations, and shapes customer experiences.

Organizations with proprietary models hold a distinct advantage. These models embed unique domain knowledge, creating competitive moats that are difficult for rivals to replicate.

Influence Beyond Boundaries

AI models exert influence that extends far beyond individual companies. Consider how recommendation systems drive consumer behavior, or how risk models set the rules for credit, fraud detection, and insurance pricing. In many industries, the algorithms themselves—not the markets—are defining the rules of engagement.

This shift explains why leaders of model ecosystems, whether large language model providers or industry-specific AI developers, wield outsized influence. They shape markets even if they are not selling products directly to end consumers.

The Rise of the AI Model Elite

Who Belongs to This Elite

The new elite falls into several groups:

• Model developers: those who own foundation models and set the standard for global AI adoption.

• Enterprise transformers: organizations that adapt general-purpose models into domain-specific solutions tailored to their industries.

• Infrastructure enablers: cloud hyperscalers and semiconductor companies that provide the compute backbone powering AI.

Together, they form an interconnected web of influence. Without them, enterprises risk dependency and diminished autonomy.

Shifting Corporate Hierarchies

As models take center stage, the balance of power within enterprises is shifting. Chief Data and AI Officers, once viewed as support functions, are becoming core strategic leaders. CIOs and CTOs increasingly shape corporate direction not through systems integration, but through model governance and orchestration.

Meanwhile, companies that rely solely on external AI providers without developing their own model capabilities risk becoming subordinates in their own industries.

Implications for Enterprises

Competitive Dynamics

The question for enterprises is no longer whether to adopt AI, but whether to own or rent it. Relying exclusively on third-party providers creates dependency, limiting differentiation. By contrast, proprietary or fine-tuned models tailored to enterprise-specific knowledge create defensible competitive advantages.

Workforce and Governance

As models become decision-making engines, corporate governance must evolve. Human oversight of model outputs, ethical guardrails, and transparent auditing will be critical. This creates new skill gaps—not only in data science and engineering, but also in AI governance, risk management, and ethics.

The organizations that close these gaps fastest will strengthen both trust and agility.

Risks of a Model-Centric Elite

Concentrating power among a small set of model builders and infrastructure providers raises systemic risks. Ethical and regulatory frameworks lag behind the technology, creating uncertainty for enterprises.

Geopolitical dynamics also come into play. Nations and corporations alike face the risk of “AI colonialism,” where access to advanced models is dictated by a handful of players, leaving others forced to adopt their rules of engagement.

How Enterprises Can Respond

Enterprises must take deliberate steps to secure their position in this new hierarchy:

• Build internal model capability: invest in domain-specific models that capture proprietary knowledge.

• Establish governance frameworks: treat model oversight as a board-level function, not just a technical concern.

• Balance partnerships with autonomy: leverage hyperscaler ecosystems, but avoid overreliance through multi-vendor strategies.

• Invest in talent: from model engineers to AI ethicists, organizations must cultivate skills for both innovation and governance.

Conclusion

Corporate power is undergoing a profound shift. The old elite controlled markets through capital and reach. The new elite control models—the engines that interpret data, drive decision-making, and set the boundaries of competition.

Enterprises that fail to invest in their own AI capabilities risk losing agency, becoming dependent on the few who do. The leaders of tomorrow will not simply use AI. They will control it, govern it, and in doing so, shape the future of their industries.