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Dominant design

A dominant design is a specific product architecture that achieves widespread market acceptance and becomes the de facto standard within a product category, defining the core features and configuration that subsequent innovations must accommodate or build upon.^[1] The concept was introduced by William J. Abernathy and James M. Utterback in their 1978 article "Patterns of Industrial Innovation," which analyzes how technological innovation evolves in industries over time. In this framework, dominant designs emerge as a pivotal transition point, stabilizing product variety after an initial period of experimentation and competition among alternative configurations.^[2] Abernathy and Utterback's model delineates three sequential phases of industrial innovation: the fluid phase, characterized by high uncertainty, frequent product innovations, and diverse competing designs driven by performance improvements; the transitional phase, where market feedback and standardization efforts lead to the selection and dominance of a single architecture; and the specific phase, marked by incremental process innovations focused on efficiency, cost reduction, and scale rather than radical product changes.^[3] This progression reflects a shift from exploring novel technologies to refining and optimizing the established dominant design, often culminating in mature market structures dominated by a few large firms.^[4] The emergence of a dominant design has profound implications for industry dynamics, including reduced technological variety, intensified competition on price and reliability, and barriers to entry for latecomers, while influencing firm strategies toward process improvements and potential technological discontinuities that may spawn new cycles of innovation.^[1] Empirical studies across sectors like semiconductors, automobiles, and consumer electronics validate the model's patterns, highlighting how dominant designs lock in technological trajectories and shape long-term competitive landscapes.^[2]

Definition and Fundamentals

Core Definition

A dominant design refers to a specific product architecture or technological configuration that achieves widespread acceptance within an industry, establishing itself as the de facto standard to which competing products must conform to remain viable.^[5] This concept, originating in innovation management, describes a design that captures the majority of the market, often exceeding 50% share, and shapes the trajectory of subsequent product development.^[1] It represents a convergence on core components and features that balance performance, cost, and user needs, stabilizing the product category.^[2] Unlike a formal standard, which is codified through institutional processes by bodies such as the International Organization for Standardization (ISO), a dominant design arises organically from market competition and user preferences without regulatory enforcement.^[6] Similarly, it differs from "best practice," which pertains to optimized operational or managerial processes across activities, whereas a dominant design focuses specifically on product-level architecture and features.^[7] The core attributes of a dominant design include market allegiance, whereby it garners broad customer and producer endorsement as the normative benchmark; technological lock-in, which entrenches the configuration and constrains alternative paths due to complementary investments and ecosystem dependencies; and influence on future variations, directing incremental innovations toward refinements within its framework rather than radical departures.^[5]^[7]

Key Characteristics

A dominant design exhibits modularity, enabling independent improvements to individual components without necessitating a complete redesign of the overall system, which fosters specialization and innovation within stable boundaries. This characteristic arises as the design stabilizes, allowing firms to refine subsystems while maintaining architectural integrity. Similarly, compatibility ensures interoperability among components and with complementary technologies, promoting standardization that reduces integration costs and barriers to adoption across the ecosystem.^[8] Scalability further defines the design by supporting mass production through standardized processes and adaptable architectures, facilitating volume efficiencies and broader market penetration without proportional increases in complexity.^[9] The dominance of such designs is reinforced by several mechanisms. Network effects amplify value as adoption grows, since the design's utility increases with the number of users and compatible products, creating a self-reinforcing cycle that discourages alternatives.^[1] Learning economies emerge through cumulative production experience, driving down costs via familiarity and process refinements, which solidify the design's economic viability over time.^[9] Institutional embedding integrates the design into regulatory frameworks, supply chains, and industry norms, embedding it deeply within organizational routines and external dependencies that resist disruption.^[10] Unlike incremental designs, which focus on marginal enhancements within an established framework, dominant designs resolve fundamental architectural uncertainties by consolidating varied product forms into a prevailing configuration, thereby stabilizing industry variety and shifting focus from experimentation to refinement.^[9] This resolution marks a pivotal transition, reducing technological ferment and enabling sustained incremental progress thereafter.^[11]

Theoretical Origins and Evolution

Origins of the Theory

The theory of dominant design emerged in the late 1970s as part of broader efforts to model technological evolution and industrial innovation patterns. James M. Utterback and William J. Abernathy laid the foundational groundwork in their 1975 paper, which proposed a dynamic model linking product and process innovations to firm strategies and production capabilities. This work emphasized how innovations shift from radical product changes in early stages to incremental process improvements as technologies mature, setting the stage for understanding design standardization.^[12] Their seminal 1978 article, "Patterns of Industrial Innovation," formalized the dominant design concept within this framework, describing it as a prevailing product configuration that gains widespread adoption and defines subsequent industry trajectories. The paper outlined three evolutionary phases—fluid (focused on diverse product innovations), transitional (marked by the emergence of the dominant design and reduced variation), and specific (centered on efficient processes)—primarily drawing from empirical observations in manufacturing sectors like automobiles.^[11] This formulation built upon Joseph Schumpeter's ideas of creative destruction, where innovative disruptions replace obsolete technologies, and nascent evolutionary economics views that treat technological progress as a Darwinian selection among variants. Early applications targeted manufacturing industries, where dominant designs facilitate economies of scale by stabilizing core architectures amid competitive pressures.^[11]

Evolution of the Concept

In the 1990s, the dominant design theory underwent significant expansion through its integration with Clayton Christensen's disruptive innovation framework, as articulated in his 1997 book The Innovator's Dilemma. This linkage underscored how the solidification of a dominant design often entrenches incumbent firms in incremental, sustaining innovations, thereby impeding their adaptation to disruptive technologies that redefine market boundaries and performance metrics.^[13] The concept also experienced interdisciplinary growth, particularly in strategic management, where it intersected with David Teece's 1986 analysis of profiting from technological innovation. Teece's framework highlighted the role of appropriability regimes—such as intellectual property protection and complementary assets—in determining how firms capture value from innovations tied to emerging dominant designs, influencing decisions on integration, licensing, and collaboration. This integration extended into economic geography, where scholars examined how regional clusters enhance the propagation of dominant designs via localized knowledge spillovers and inter-firm interactions that accelerate standardization. Key milestones in this evolution included Marco Iansiti's 1990s research on design hierarchies, which delineated how dominant designs stabilize at the architectural core of products—such as in computing systems—while permitting ongoing modular improvements at lower levels to sustain competitive evolution. Further refinement occurred in Henry Chesbrough's 2003 open innovation paradigm, which emphasized how permeable organizational boundaries and external technology sourcing reshape the emergence and iteration of dominant designs in networked ecosystems.^[14] These developments bridged to contemporary challenges, such as adapting dominant designs to the fluidity of digital platforms and ecosystems.

Emergence Process

Stages of Formation

The formation of a dominant design in an industry follows a sequential process outlined in the industry life cycle model proposed by Abernathy and Utterback, where the dominant design emerges as a pivotal shift from radical, performance-oriented innovation to more incremental, efficiency-driven advancements.^[11] This model describes three distinct phases—fluid, transition, and specific—that characterize the evolution of technological and market dynamics leading to design standardization.^[11] In the initial fluid phase, industries exhibit high variety in product designs as firms engage in extensive experimentation to maximize performance and address uncertain market needs.^[11] Innovation is predominantly radical and product-focused, with numerous competing architectures emerging from small-scale or custom applications, driven by technological uncertainty and low emphasis on production efficiency.^[11] Market feedback is limited, and sales growth is slow, as the primary goal is to explore diverse solutions without a clear path to dominance.^[11] The transition phase marks a critical turning point, where increasing returns to adoption begin to favor certain designs through market selection mechanisms.^[11] Design diversity reduces as standardization of key components and subsystems occurs, influenced by user preferences and competitive pressures that amplify the advantages of scalable configurations.^[11] Innovation shifts toward refining a leading architecture, with growing market acceptance and interactions between producers and consumers accelerating the consolidation process.^[11] Finally, the specific phase represents lock-in to the dominant design, where the industry stabilizes around a prevailing configuration that defines the core technology and architecture.^[11] Emphasis turns to cumulative innovation, focusing on process improvements for efficiency, cost reduction, and reliability, as radical changes become rare and the market structure often consolidates into fewer competitors.^[11] This phase underscores the dominant design's role in enabling scaled production and incremental enhancements over disruptive experimentation.^[11]

Influencing Factors

The emergence of a dominant design is shaped by a variety of market factors that drive convergence toward a preferred configuration. Customer preferences play a pivotal role, as users often favor designs that best meet evolving performance criteria, such as reliability and usability, leading to the selection of variants that align with dominant user needs during periods of technological variation. Economies of scale further accelerate this process by rewarding designs that enable cost reductions through increased production volumes, making them more attractive to manufacturers and consumers alike. Sponsor involvement, including lead users who articulate advanced needs and governments providing funding or procurement, can tip the balance by endorsing specific designs, as seen in early adoption by influential buyers that signal market viability. Technological factors also significantly influence the trajectory toward dominance. Complementary assets, such as manufacturing capabilities and distribution channels, allow firms controlling these resources to integrate and promote a design more effectively, capturing value from innovation even if they did not originate the core technology.^[15] Patent thickets—dense webs of overlapping intellectual property rights—can complicate emergence by creating barriers to entry and negotiation costs, potentially delaying consensus on a single design unless resolved through licensing or pooling.^[2] Architectural efficiency, referring to modular and scalable structures that facilitate incremental improvements, enhances a design's longevity and adaptability, making it more likely to prevail over rigid alternatives. Institutional factors provide the broader context for dominant design formation. Regulatory standards imposed by governments or industry bodies can mandate compatibility, hastening convergence, as in telecommunications where interoperability requirements favor unified architectures.^[16] Supplier networks contribute by standardizing components and reducing transaction costs, enabling ecosystems where a dominant design benefits from reliable, interoperable supply chains. Cultural acceptance influences adoption rates, with designs embedding societal values—such as safety norms in automotive industries—gaining traction through collective endorsement and reduced resistance. Despite these enablers, several barriers can impede or distort the emergence of a dominant design. Path dependence arises when early choices lock in trajectories due to historical contingencies, often resulting in suboptimal outcomes reinforced by learning effects and coordination challenges. Increasing returns exacerbate this, as adoption of an initial design generates positive feedback loops—through network effects or cumulative investments—that entrench it against superior alternatives, exemplified by the QWERTY keyboard layout, which persisted despite more efficient options due to typing skill investments and typewriter standardization in the late 19th century.^[17]

Empirical Evidence and Examples

Historical Industry Examples

In the automobile industry during the early 1900s, the Ford Model T exemplified the emergence of a dominant design through its standardized architecture, which integrated an internal combustion engine, a unified chassis, and a modular body configuration optimized for mass production via the moving assembly line introduced in 1913.^[18] This design resolved key uncertainties in vehicle configuration, such as propulsion and structural layout, allowing Ford to produce over 15 million units by 1927 and capturing more than 50% of the U.S. market share at its peak.^[19] Prior to this, the industry featured diverse experimental forms including steam-powered and electric vehicles, but the Model T's architecture became the de facto standard, influencing subsequent innovations toward incremental refinements rather than radical redesigns.^[20] The typewriter industry provides another historical case, where the QWERTY keyboard layout, patented in 1878 by Christopher Latham Sholes and adopted by Remington, established dominance despite not being the most efficient arrangement for typing speed. Developed to prevent mechanical jamming in early typewriters by separating common letter pairs, QWERTY benefited from path dependence through network effects: as typists learned the layout and typewriter schools standardized training around it, switching costs escalated, entrenching it against alternatives like the Dvorak Simplified Keyboard proposed in 1936.^[21] By the mid-20th century, QWERTY's lock-in extended to electronic keyboards, illustrating how historical contingencies and increasing returns perpetuated a suboptimal design across generations of technology.^[22] In the television sector from the 1950s to the 1970s, the cathode ray tube (CRT) architecture transitioned from black-and-white to color dominance, standardizing scan-line resolutions such as the 525-line NTSC system approved by the FCC in 1953.^[23] This design, building on earlier mechanical scanning experiments, resolved format battles by integrating electron beam deflection and phosphor coatings for color reproduction, enabling widespread adoption with U.S. color TV penetration reaching 50% of households by 1972.^[24] The CRT's prevalence reduced architectural variety, as firms like RCA and Sony focused on improving tube durability and resolution rather than pursuing alternative display technologies like plasma in this era.^[2] Empirical studies by Abernathy and Utterback, analyzing industries including automobiles and semiconductors, demonstrate that dominant design emergence correlates with a sharp decline in product variant diversity: for instance, major innovations totaled approximately 100 before 1908 in autos, dropping to about 2-3 annually post-dominance, as competition shifted to efficiency and scale.^[11] Their model, drawn from historical data on 17 U.S. industries, shows this pattern holds across sectors, with design stabilization occurring within 5-10 years of market formation and leading to oligopolistic structures.^[4]

Contemporary and Digital Examples

In the smartphone industry, the introduction of the iPhone in 2007 marked a pivotal shift toward a dominant design centered on multi-touch capacitive touchscreen interfaces and app ecosystems, supplanting earlier stylus-based and physical keyboard-dominant models.^[25] This design emphasized intuitive user interaction and multimedia capabilities, leading to widespread adoption as touchscreen-equipped smartphones rose from near 0% of the market in early 2007 to over 90% by 2012.^[26]^[27] The iOS and Android operating systems further solidified this paradigm through their open app stores, fostering network effects that encouraged developer ecosystems and user lock-in, with Android capturing 68.8% global market share by 2012 while iOS maintained a strong 18.8% position.^[28] In the electric vehicle (EV) sector during the 2010s, lithium-ion batteries emerged as the core of an evolving dominant design, offering high energy density and cycle efficiency of around 86%, far surpassing alternatives like nickel-metal hydride.^[29] This battery technology, paired with modular chassis architectures, enabled scalable production and easier upgrades without full vehicle redesigns, addressing key barriers to EV adoption; as of 2024, lithium-ion holds over 95% of the EV battery market.^[30] Tesla played a significant role in advancing this design through innovations like its integrated powertrain and partnerships, such as supplying components to Daimler by 2014, though no single firm has achieved full market dominance amid ongoing competition from incumbents and new entrants.^[29] The Transformer architecture, proposed in 2017 by Vaswani et al., represents an emerging dominant design in artificial intelligence, particularly for machine learning tasks involving natural language processing and generative models.^[31] This attention-based framework, which dispenses with recurrent and convolutional layers, enabled breakthroughs in translation and parsing, achieving state-of-the-art BLEU scores of 28.4 on English-to-German and 41.8 on English-to-French benchmarks while training 3.5 times faster than prior models.^[31] By 2023, it had become the foundational paradigm for leading AI systems, powering multimodal capabilities and contributing to platform lock-in through business model innovations like AI-as-a-service; as of 2025, it remains the core architecture for major large language models.^[32]^[33] Studies indicate this convergence facilitates rapid progress in digital industries via standardized architectures that amplify network effects among developers and users.^[32]

Implications for Innovation and Industry

Effects on Innovation Patterns

The emergence of a dominant design fundamentally alters the trajectory of innovation within an industry, shifting from a fluid phase dominated by radical, experimental product variations to a more structured era of cumulative, efficiency-oriented improvements. In the pre-dominant phase, innovation patterns are characterized by high uncertainty and frequent major changes in product architecture, with firms exploring diverse concepts to meet emerging market needs; this fluid pattern emphasizes radical innovations that redefine core functionalities. Once a dominant design solidifies—often through selection mechanisms like market feedback and standardization—innovation transitions to a specific pattern, where efforts focus on incremental enhancements to product reliability, cost reduction, and process efficiency within the established architecture, reducing the pace of radical experimentation. This shift reinforces incumbents' existing competencies by aligning R&D with sustaining innovations that build upon the dominant architecture, yet it simultaneously creates vulnerabilities to competence-destroying disruptions. Established firms, having invested heavily in capabilities optimized for the dominant design, excel at competence-enhancing innovations such as refining components or processes but struggle to recognize or pursue architectural shifts that redefine linkages between components, often leading to organizational inertia. Dominant designs thus trap incumbents in trajectories that prioritize short-term efficiency over long-term adaptability, as their processes and values are tuned to serve high-end customers demanding incremental improvements rather than low-end or new-market disruptions.^[34] Over the long term, dominant designs lead to reduced emphasis on architectural innovation—the reconfiguration of how components interact—while channeling R&D toward component-level advancements, fostering specialization but risking technological plateaus where marginal gains yield diminishing returns. This pattern can stabilize industries around the dominant architecture for decades, as seen in the automobile sector post-Model T, where innovation concentrated on engine and chassis refinements rather than wholesale redesigns, potentially delaying responses to paradigm-shifting technologies.^[35] Such plateaus heighten the appeal of external disruptions, underscoring the dual role of dominant designs in both enabling sustained progress and constraining broader exploratory efforts.

Impacts on Competitive Dynamics

The emergence of a dominant design fundamentally reshapes competitive dynamics by favoring incumbents with complementary assets, such as manufacturing scale and distribution networks, which leads to market consolidation and the formation of oligopolies. In the post-dominant design phase, competition shifts from diverse product variations to efficiency in production and cost reduction, erecting high barriers to entry for new firms due to the need for substantial capital investments and integrated processes. For instance, in the automobile industry, the Ford Model T established the dominant design around 1908, emphasizing mass production and standardization, which by 1937 resulted in a dominant-firm oligopoly where General Motors, Ford, and Chrysler controlled approximately 85% of U.S. sales.^[36]^[37] This consolidation reduced the number of viable competitors, as smaller entrants struggled against the scale advantages of established players. Strategically, firms that achieve or early adopt the dominant design gain first-mover advantages through market share capture and learning effects, but they face significant risks from imitation by rivals seeking to replicate the standard. To mitigate imitation and expand influence, leaders often pursue alliances or licensing agreements, allowing controlled diffusion while reinforcing their position. In the typewriter industry, for example, early entrants like Underwood and Remington survived the dominant design era (around 1900) by leveraging process innovations, while licensing helped standardize the QWERTY keyboard and visible writing mechanisms, limiting new entrants to just five major firms by 1940. Such strategies enable incumbents to transform technological leadership into enduring competitive edges, though they require balancing openness for adoption against protecting core innovations.^[36]^[38] Dominant designs also create competitive traps through technological lock-in, fostering inertia among incumbents who become entrenched in the prevailing architecture and resistant to radical changes. This rigidity opens opportunities for niche challengers to disrupt via modular alternatives that address underserved segments, bypassing the need to compete directly on the dominant platform. In the information technology sector, for instance, startups entering after dominant designs emerged in dominance battles (1979–2007) often succeeded by targeting non-platform niches, exploiting incumbents' integrative commitments and limited adaptability. Such disruptions highlight how lock-in can erode incumbents' advantages, allowing modular innovations to gradually erode market share in overlooked areas.^[36]^[39]^[40]

Role in Intellectual Property Strategies

Dominant designs often reinforce intellectual property (IP) strategies through extensive patenting of core architectural elements, creating so-called patent thickets that form dense webs of overlapping rights around key technologies. These thickets deter entrants by increasing the legal and transactional costs of innovation, as competitors must navigate or license multiple patents to access the dominant architecture.^[41] A prominent example is Qualcomm's IP approach in mobile chipsets, where its vast portfolio of patents on wireless communication standards has established a thicket protecting its dominance in 5G and CDMA technologies, enabling royalty extraction from device manufacturers worldwide.^[42] When de facto dominant designs evolve into industry standards, competition authorities may impose licensing obligations akin to fair, reasonable, and non-discriminatory (FRAND) terms to balance IP exclusivity with the need for interoperability among ecosystem participants, particularly where formal standardization body approval is absent. Such mechanisms mitigate hold-up risks in collaborative settings but can lead to disputes over royalty rates and licensing scope, as seen in telecommunications where dominant designs underpin essential patents for network compatibility.^[43]^[44] Firms strategically deploy a mix of IP tools beyond patents, using trademarks and copyrights to exert control over ecosystems built around dominant designs. Trademarks protect branding elements that signal compatibility and quality within the design's architecture, fostering consumer lock-in and supplier adherence, while copyrights safeguard software interfaces and documentation integral to modular implementations.^[45] In modular dominant designs, however, this layered IP approach heightens hold-up problems, where upstream component owners can opportunistically demand excessive royalties after downstream investments are sunk, complicating ecosystem coordination.^[46] Recent studies from 2023, 2024, and 2025 highlight evolving IP tensions in AI-dominant designs, where open-source models challenge traditional proprietary strategies by accelerating adoption but raising concerns over insufficient protection for foundational architectures. These analyses emphasize the friction between open-source licensing, which promotes rapid diffusion, and patent-driven exclusivity needed to recoup AI R&D costs, potentially fragmenting ecosystems as firms weigh collaborative openness against competitive risks.^[47]^[45]

Criticisms and Contemporary Challenges

Theoretical Debates and Limitations

The dominant design theory, originally formulated in the context of manufacturing industries, has faced debates regarding its universality across sectors such as services and software, where modular architectures and ecosystem dynamics prevail over rigid product standardization. Scholars argue that the theory's emphasis on a singular, stable design emerging through market selection does not fully capture the fluid, multi-sided interactions in platform-based industries, as highlighted in platform leadership frameworks that prioritize supply- and demand-side economies of scope for ongoing innovation rather than convergence to one dominant artifact.^[48] This contrast suggests that dominant design applies less straightforwardly to intangible, user-centric domains like software, where designs evolve through complementary ecosystems rather than path-dependent lock-in.^[48] A key limitation of the theory lies in its overemphasis on post-emergence stability, which overlooks the co-evolutionary processes between technologies and users that shape design trajectories from the outset. Traditional models treat users as passive selectors, focusing on market inertia and network effects to explain convergence, yet empirical analyses reveal that users actively influence design through dynamic feedback loops, leading to less predictable outcomes than the theory anticipates.^[49] Furthermore, the framework struggles to account for fragmented markets, where multiple co-existing designs persist due to niche demands or institutional barriers, failing to predict such non-convergent scenarios.^[50] Empirical critiques underscore these conceptual gaps, particularly in knowledge-intensive fields like biotechnology, where dominant designs often emerge delayed or remain absent in many subdomains owing to stringent regulatory hurdles and high scientific uncertainty. A longitudinal analysis of U.S. patents from 1976 to 2003 found that only 13 of 27 biotechnology components transitioned to a stable growth phase indicative of dominant design formation, with the remainder stalled in exploratory stages due to factors including patent legitimacy pressures and regulatory oversight that prolong variation without resolution.^[51] These findings from 2000s research illustrate how external institutions can disrupt the expected pattern of design dominance, challenging the theory's generalizability to regulated sectors.^[51] Methodologically, the reliance on historical case studies—such as automobiles or typewriters—poses significant issues, as inconsistencies in defining the unit of analysis, temporal boundaries, and causal mechanisms limit the theory's predictive power and replicability across diverse contexts. Studies highlight anomalies, like the absence of inverted-U entry/exit patterns in complex industries, which arise from varying levels of technological granularity and fail to hold universally, underscoring the need for more standardized, multi-level approaches to empirical validation.^[50] Over time, the concept has evolved through such refinements to incorporate nested hierarchies of designs, yet core methodological challenges persist.^[50]

Adaptations in the Digital and AI Era

In the digital era, dominant designs have emerged more rapidly due to the scalability of platforms and network effects, which accelerate the selection process compared to traditional industries. For instance, API standards in cloud computing, such as RESTful architectures, have become de facto dominant designs by enabling seamless interoperability across services, allowing providers like Amazon Web Services to capture significant market share through standardized interfaces that reduce integration barriers.^[52] This faster pace is facilitated by digital platforms that lower entry costs for experimentation, leading to quicker convergence on architectural standards.^[53] The boundaries between product and service dominance have blurred in digital ecosystems, where offerings increasingly combine hardware, software, and data-driven services into integrated solutions. In cloud computing, for example, dominant designs now encompass not just technological components but also service delivery models like Platform-as-a-Service (PaaS), where providers bundle APIs with scalable infrastructure to create hybrid value propositions that defy traditional product-service distinctions.^[54] This shift emphasizes ecosystem orchestration over isolated products, as seen in how Google Cloud's API ecosystem integrates AI services with data pipelines to sustain competitive advantage.^[55] In the AI domain, modular architectures such as transformer-based neural networks enable rapid iterations and shifts, challenging the traditional notion of lock-in associated with dominant designs. Unlike rigid hardware paradigms, these modular components—exemplified by interchangeable layers in models like GPT—allow developers to recombine elements without vendor-specific dependencies, fostering a fluid evolutionary landscape where no single configuration achieves permanent dominance.^[56] This modularity, highlighted in analyses of AI's technological trajectory, questions the durability of lock-in by promoting open-source diffusion and collaborative refinement.^[57] Data has emerged as a critical complementary asset, serving as the foundational input for training and refining AI models, much like raw materials in manufacturing, but with network effects amplifying its value in dominant configurations.^[58] Looking ahead, hybrid models are integrating dominant designs with broader ecosystems, where AI standards coexist with regulatory frameworks to balance innovation and oversight. For example, regulatory frameworks like the EU AI Act (2024), which entered into force in August 2024 and applies general-purpose AI rules from August 2025, promote transparency and ethical AI development through risk-based obligations, influencing standardized practices in AI ecosystems.^[59] Ongoing global efforts toward regulatory harmonization as of 2025, such as those discussed in UN and EU initiatives, may further shape interoperable AI architectures and mitigate risks like monopolistic control while supporting scalable AI deployment.^[60]^[61] The original dominant design theory, rooted in cumulative selection processes, reveals gaps in accounting for the amplified role of algorithms and user-generated content in digital contexts. Algorithms on platforms like social media actively curate visibility, influencing design selection by prioritizing content that maximizes engagement metrics, thus accelerating the dominance of user-favored architectures over market-driven ones.^[62] Similarly, user-generated content contributes to emergent standards, as seen in how community-driven refinements on platforms like GitHub shape AI toolkits, introducing decentralized dynamics absent from traditional theory.^[63]

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