Woofun AI notes that the fundamental logic of corporate competition is undergoing a radical transformation as artificial intelligence becomes universally accessible. The traditional premise that resource accumulation determines victory is collapsing, replaced by a new reality where companies utilizing identical large models and intelligent agents exhibit diverging performance trajectories. Some organizations are leveraging these technologies to achieve rapid ascent, while others, despite heavy investment, see little tangible return, signaling that the source of advantage has shifted from resource possession to transaction structure design.

The divergence in outcomes is starkly illustrated by the contrasting experiences of Duolingo and McDonald's in the recent AI adoption cycle. In 2023, Duolingo integrated the GPT-4 model, the same technology available to countless competitors, to launch its Duolingo Max platform. Rather than deploying the AI as a simple question-and-answer utility, the company embedded it directly into its subscription-based, gamified business model. The resulting AI-powered dialogues and intelligent answer explanations created compelling new value propositions that drove significant increases in user engagement and subscription rates. This success was not derived from superior model access but from the strategic integration of AI into a unique transactional framework that altered the value exchange with users.

In sharp contrast, McDonald's represents the archetype of substantial investment yielding minimal structural change. The fast-food giant has poured resources into smart menu recommendations, voice ordering systems, and collaborative AI initiatives with major technology firms.

However, these implementations have largely functioned as efficiency patches applied to existing operational workflows without fundamentally altering the transaction structures between the corporation, its franchisees, suppliers, and customers. Consequently, no significant competitive barriers have been erected, and the company remains vulnerable to the same market forces as before. This case underscores that applying AI to legacy processes without redefining the underlying economic relationships fails to generate sustainable differentiation.

Industry surveys further corroborate this trend, revealing that the majority of generative AI pilot projects across various sectors have consumed significant capital without delivering proportional returns. The prevailing pattern involves using AI to optimize old processes, resulting in marginal efficiency gains but no structural innovation. The landscape changed dramatically in early 2025 when DeepSeek released its top-tier models as open-source, drastically reducing the cost of access and making advanced AI capabilities widely available. In this environment, model quality alone is insufficient to create a competitive edge, as the technology itself has become a commodity. The critical differentiator is no longer the availability of the tool but the architecture of the business model into which it is integrated.

Many entrepreneurs currently face a paradoxical dilemma: they fear obsolescence if they do not adopt AI, yet adoption often fails to widen their competitive gap, leaving them competing on traditional factors like capital and manpower. This leads to homogeneous competition and inward-focused rivalry. The root cause lies in an outdated assumption that advantages stem from inherent resource endowments and that value, scarcity, and imitatability are intrinsic attributes of those resources. While this logic held in an era of stable business models, the ability of AI to drastically reduce the cost of creating new models has rendered the resource-based view ineffective. Sustainable advantage now arises from the unique transaction structures that AI enables, which determine which resources are valuable and which capabilities can create barriers.

To clarify the mechanics of this shift, competition can be categorized into four distinct dimensions based on the alignment of strategy and business model. In the first category, efficiency competition occurs when companies share the same strategy and model, such as two supermarkets on the same street or factories in the same industry. Here, AI serves merely as a tool to enhance operational efficiency. While both parties can optimize their operations, any short-term cost advantages will eventually disappear as technology becomes ubiquitous, forcing competition back to traditional resources like supply chains and capital. This is the only realm where the traditional resource-based view remains valid.

The second category, model competition, arises when companies pursue the same strategy but employ different business models. Consider two firms providing financial services to small and medium-sized enterprises: one charges per person-day, while the other utilizes AI for standardized compliance reviews and charges an annual subscription fee. Although the customer base is identical, the transaction structures are fundamentally different. In this scenario, AI is not a patch but a prerequisite for a new business model. Once implemented, the new pricing logic, customer segmentation, and profit distribution rules become difficult to replicate, even if competitors acquire the same AI technology. This structural difference allows latecomers with fewer resources to overtake established rivals.

Platform competition constitutes the third category, characterized by different strategies but the same underlying model. Services like group buying and food delivery rely on the core principle of connecting suppliers and demanders to earn transaction fees. AI in this context amplifies scale and enhances network effects; more precise matching improves user experience, attracting more merchants and generating more data, which in turn refines the AI. The barrier here lies in the entire structural framework and the resulting data loops, rather than any single resource. The fourth category, ecosystem competition, involves different strategies and different models, such as AI-driven platforms for scientific research or personalized education. These ecosystems emerge from AI and allow early entrants to establish dominance by designing unique transaction structures that create new rules, user habits, and data loops that do not exist in traditional frameworks.

The patterns across these four categories reveal a clear hierarchy: the more similar the models, the more AI functions as a tool to amplify existing resources, and the more temporary the resulting advantages. Conversely, the greater the differences in models, the more AI plays a role in reshaping transaction structures. Advantages derived from similar models are often fleeting and can be overcome through mergers or acquisitions. Only advantages arising from unique transaction structures that create new types of resources—such as subscription-based retention systems, multi-party profit-sharing networks, or AI-generated data loops—constitute truly irreversible barriers. Homogeneous models can create temporary advantages based on resource differences, but only unique transaction structures can create lasting moats.

The theory of transaction structures provides a unified framework to explain both stable competition in mature industries and the disruption caused by innovative models. A transaction structure encompasses both value creation and value distribution, addressing how stakeholders collaborate and how profits, costs, and risks are shared. As Raffi Amit of Harvard Business School has proposed regarding "activity systems", a business model is a system of interdependent activities extending beyond a single company's boundaries. The transaction structure concept goes further by focusing on the fundamental relationships: who interacts with whom and according to what rules. This perspective shifts the basic unit of competition from the internal resources of a single company to the entire value system involving multiple entities.

Crucially, the value of resources is defined by the transaction structure, not inherent properties. A physical store may be an asset in a traditional distribution model but a burden in a direct-sales subscription model. Similarly, user data may be a byproduct in a single transaction but extremely valuable in a multi-party profit-sharing platform. Since value is derived from the structure, attributes like scarcity and imitatability are not inherent to the resource but are conferred by the specific structure. This explains why core assets can become liabilities during transformation when the dominant transaction structure changes. The source of competitive advantage lies in the structure itself, which precedes resources and defines their value. The ability to design effective transaction structures is the key capability, but it is not an independent source of advantage separate from the structure it creates.

True barriers are structural in nature because a complete set of transaction structures is an interconnected network that is extremely difficult to mimic. Single technologies can be purchased, talents recruited, and products copied, but replicating a structure requires redefining responsibilities, sharing mechanisms, and rules with all partners, as well as rebuilding trust. The commonly cited barriers to imitation—causality ambiguity, social complexity, and path dependence—are systemic features of multi-party transaction structures rather than attributes of individual resources. Therefore, barriers to business model innovation are significantly deeper than those to technology or product innovation. A correct structure can transform ordinary technologies into effective barriers, while a wrong structure can render even the most advanced technologies ineffective in a homogeneous market.

The evolution of AI-driven companies can be mapped through four stages based on the relationship between humans and AI: tool, partner, and intelligent entity. In the enabling stage, AI serves as a tool to support existing business models. While the internal structure may remain unchanged, leading companies can use user data generated by AI to reframe their business models, transforming basic operations into sophisticated services like customized solutions and performance-based profit distribution. In this stage, the same user data becomes a unique core asset due to the new transaction structure, creating barriers that competitors cannot replicate even with the same technology.

The framework-building stage involves establishing a complete end-to-end framework by breaking down departmental barriers and organizing teams around value-based goals. In the custom furniture industry, for example, some companies form closed-loop teams responsible for the entire process from measurement to after-sales service, rather than dividing design, production, and installation into separate steps. While anyone can purchase the AI technology, the structural elements such as responsibilities, coordination rules, and evaluation mechanisms of these teams are extremely difficult to replicate. Companies adopting these structures maintain a competitive edge even if competitors acquire the same AI tools.

In the AI-led stage, AI plays a central role in key processes, taking responsibility for results while the organization continues to operate using traditional divisions of labor. Many e-commerce companies have automated refund reviews using AI, redefining compensation mechanisms between platforms, consumers, and merchants. Competitors can build similar systems but cannot replicate the specific rules and risk control mechanisms that have evolved over time. The longer a company uses this approach, the stronger its competitive barriers become. The final integrated stage achieves a trinity of human-intelligent entity-intelligent system coexistence, where value delivery, capability development, and rule evolution occur dynamically. Coordination relies on internally accepted shared rules rather than hierarchical commands, replacing bureaucratic structures with flexible collaboration models.

Achieving these stages requires three key shifts in thinking. First, companies must move away from a technological arms race, recognizing that models, hash rate, and AI talent can be acquired and replicated but do not constitute long-term barriers. The real challenge is integrating AI into better transaction structures. Second, model innovation must be initiated at any stage, not waiting for full organizational readiness. The sooner structural differences are identified, the sooner companies can escape competitive inertia. Third, strategic focus must shift from resource-based thinking to design-based thinking. Companies should first design an optimal transaction structure and then determine the resources and capabilities required, acquiring or collaborating to fill gaps. Design comes first, and capabilities serve as the foundation for implementation.

It is essential to distinguish between two levels of capabilities in this context. First-level capabilities enable value creation within a given business model, such as supply chains, brands, and execution capabilities. Their value is defined by the transaction structure, and changing the structure can turn them from assets into burdens. These correspond to the traditional resource-based view. Second-level capabilities enable the design and restructuring of transaction structures, involving identifying limitations, designing new structures, and implementing them iteratively. These are the true drivers of structural change and represent what David Teece referred to as "dynamic capabilities". Many current debates about investing in capabilities stem from confusing these two levels.

For most companies today, the question is no longer whether to adopt AI but how to maintain a competitive edge when all competitors have access to the same technology. The answer lies not in the technology department but in the design of the business model. In the next decade, the most successful companies will not necessarily be those that possess the most AI resources but those that first use AI to restructure their transaction structures and reshape their relationships with stakeholders. Because technology will eventually become widespread, it is the structure that determines who will prevail. This marks a definitive shift where the ability to architect value systems supersedes the mere accumulation of technological assets.