AI 산업 혁신특성 분석 연구 - AI알고리즘 및 모델 부문을 중심으로 -

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AI 산업 혁신특성 분석 연구 - AI알고리즘 및 모델 부문을 중심으로 - An Analysis of Innovation Characteristics in the AI Industry: Focusing on the AI Algorithm and Model Sector

AI 산업 혁신특성 분석 연구 - AI알고리즘 및 모델 부문을 중심으로 - 원문 다운로드 AI 산업 혁신특성 분석 연구 - AI알고리즘 및 모델 부문을 중심으로 - 원문보기

보고서명
AI 산업 혁신특성 분석 연구 - AI알고리즘 및 모델 부문을 중심으로 -
보고서명(영문)
An Analysis of Innovation Characteristics in the AI Industry: Focusing on the AI Algorithm and Model Sector

책임자 조원선
소속기관과학기술정책연구원
내부연구참여자김석관,이광호,최해옥,김권일,이승윤,김지선,유제현
외부연구참여자
발행기관 과학기술정책연구원
ISBN979-11-24145-21-0
출판년도2025
페이지172
보고서유형 연구개발적립금보고서
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표준분류 과학기술 > 과학기술일반
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주제어인공지능,AI 모델,혁신,안보,혁신 생태계

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■연구 목적
○ 본 연구는 AI 알고리즘 및 모델 부문에서 나타나는 혁신특성을 분석을 통해 AI 강국 도약을 위한 조건을 탐색하고 한국의 AI 안보 및 경쟁력 강화를 위한 전략적 방향성을 탐구하고 제시하는 목적을 가짐

■주요 내용
○ AI 모델 부문 기술 발전 궤적과 안보위협 분석
- AI 모델 기술 발전에 따른 경쟁 패러다임을 분석, AI 모델 파라미터 수를 중시하는 양적 경쟁에서 데이터(D)-인재(H)-컴퓨팅인프라(C) 통합적 역량에 초점을 맞춘 질적구조적 경쟁으로 변화를 규명
- AI 지식 생산확산구조를 분석하고, 국제정치적 구조가 AI 혁신생태계에 미치는 영향을 탐구
○ AI 모델 경쟁력의 결정요인과 국제정치 동학 탐구
- AI 모델 개발 및 경쟁력 결정요인으로 데이터(D), 인재(H), 컴퓨팅인프라(C)를 제시하고 각 분야 및 요소 간 상호작용에서 나타나는 기술경제적 병목을 유형화
- AI 모델의 기술경제적 병목이 국제정치적 환경변화에 따라 전략적 초크포인트로 변화 분석
○ AI 산업 혁신특성 분석 프레임워크 제안 및 분석
- AI 모델은 시스템 기술로서 데이터(D), 인재(H), 컴퓨팅인프라(C)라는 핵심투입요소들 중 가장 약한 고리가 시스템 전체의 한계를 결정함
- 이는 단일 요소에 대한 제약만으로 국가 AI 혁신 역량 전체가 제동될 수 있음을 의미함
- AI 모델 부문에서는 기술 자체의 우수성만으로는 지속가능한 경쟁력 담보가 불가능하고 국제정치(P)맥락에서 핵심 투입요소(DHC)에 대한 전략적 자율성이 국가 AI 역량의 궁극적 한계를 결정
○ AI 모델 부문 특허데이터 분석을 통한 글로벌 경쟁 구조 진단
- AI 알고리즘 및 모델 부문 특허 267,519건을 대상으로 정량 분석을 수행
- 딥러닝, 자연어처리, 컴퓨터 비전, 생성 모델, 하드웨어를 5대 기술군으로 분류 및 국가별 기술 전략 경쟁우위 분석의 계량적 기반을 마련
○ AI 모델 혁신특성 기반 한국 AI 경쟁력 확보의 전략적 방향성을 제안

■정책 대안
○ AI 혁신특성 기반 AI 강국 도약을 위한 전략적 방향성 설정
○ 핵심 병목(D-H-C)의 구조적 취약성 극복 및 전략적 자율성 확보
○ AI 부문 전략경쟁 심화에 대응하기 위한 능동적 기술외교 전략과 매력적인 혁신 국가로의 도약

An Analysis of Innovation Characteristics in the AI Industry: Focusing on the AI Algorithm and Model Sector

ㆍProject Leader: Onesun Cho
ㆍParticipants: Seok-Kwan Kim ･ Kwangho Lee ･ HaeOk Choi ･ Kwonil Kim ･ SeungYoon Lee ･ Jiseon Kim ･ Jaehyun Yoo

1.Research Background and Necessity
Artificial Intelligence (AI), recognized as a general purpose technology (GPT), is rapidly emerging as a critical driver of economic growth and a central pillar of national sovereignty and economic security. Consequently, the global pursuit of leadership in this field has transcended mere industrial rivalry and evolved into a high stakes arena of national strategy and intense strategic competition. In this era of intensifying concerns regarding techno economic security, Artificial Intelligence represents a strategic imperative where economic and security interests are inextricably linked.
The present study focuses specifically on the innovation characteristics of the sector concerning algorithms and models, particularly foundation models, which constitutes the core of sovereignty in the digital age. A critical insight driving this research is that innovation in such models exhibits a unique bottleneck structure. This structure requires the non substitutable combination of three key inputs, namely data(D), specialized human talent(H), and extensive computing infrastructure(C). These technological bottlenecks are susceptible to transformation into strategic chokepoints under the influence of the international political environment.
Existing research often concentrates narrowly on the technical or economic dimensions of the field and fails to adequately analyze the structural impact of geopolitical shifts, such as the securitization of technology and the strategic competition between the United States and China, on the innovation system. To address this gap, this study proposes an integrated analytical framework: the DHCP framework. The objective is to elucidate the interactions and bottlenecks among the D-H-C inputs and analyze how these combine with political factors (P) to create asymmetric interdependence and the potential for weaponization, ultimately deriving strategic directions for middle powers like South Korea.

2.Key Findings (1): Technological Trajectory and Security Threats in the AI Model Sector
AI models function as a foundation of general purpose for specialized applications. The competitive focus in the development of these models has evolved significantly. Architectural innovations such as the Transformer enabled parallel processing while paradigms of learning advanced through pre training and fine tuning as well as alignment techniques, notably reinforcement learning from human feedback. The paradigm of competition has shifted based on the primary bottleneck among the core resources, which comprise data, human talent, and computing infrastructure.
1st Generation (Scale): Driven by Scaling Laws, the focus was on maximizing parameters. Computing Infrastructure (C) was the critical bottleneck.
2nd Generation (Balance): The Chinchilla study highlighted the limitations of scale alone, emphasizing the need for balanced scaling of model size and data. Data (D) re-emerged as the key bottleneck.
3rd/4th Generation (Reasoning): The focus shifted to complex reasoning capabilities (e.g., Chain-of-Thought) and Inference-time Scaling. Human Talent (H)—the capacity for sophisticated algorithmic design—became the critical differentiator, requiring a systemic integration of D-H-C.
The competition has thus evolved from a quantitative race for resources to a qualitative and structural competition focused on the strategic integration of D-H-C,
The ecosystem of Artificial Intelligence thrives on dense networks of collaboration and a high degree of mobility among talent. However, the production of knowledge is highly asymmetric and forms a structure of hubs and spokes centered around the United States and China, both of which monopolize key resources. The increasing securitization of Artificial Intelligence poses a significant threat to this global stability. Strategic competition restricts the free flow of talent and severely hinders the transfer of tacit knowledge, which refers to the undocumented expertise that is crucial for the development of frontier models. This dynamic reinforces the dominance of hub nations and transforms the interdependence of innovation into a vector for strategic weaponization.
Traditional economic theories typically assume the substitutability of inputs. However, AI model function as a system technology exhibiting a form of complementarity akin to the Leontief model. In this context, data, human talent, and computing infrastructure are non substitutable. A deficiency in any single factor cannot be compensated for by an abundance in others, which is a characteristic that fundamentally distinguishes the sector of Artificial Intelligence from traditional industries.

3.Key Findings (2): Analytical Framework (DHCP) and Weaponized Interdependence
Traditional theories of innovation fail to capture the dynamics of the sector concerning models of Artificial Intelligence because they overlook international strategic competition and the specific vulnerability created by the bottleneck structure of the field. This structure, which is akin to the Leontief model, is susceptible to external strategic control, an occurrence which manifests as the chokepoint effect.
The sector of Artificial Intelligence models exhibits distinctive structural characteristics that fundamentally shape its innovation dynamics. Central to these characteristics is the formation of a global and asymmetric structure of hubs and spokes, which emerges from and is perpetuated by two interconnected mechanisms. First, path dependence creates profound effects of lock in within the ecosystem. This manifests most notably in the industry wide entrenchment in specific technology stacks, exemplified by the dominance of the NVIDIA CUDA ecosystem, and in the geographic concentration of knowledge clusters, particularly the talent agglomeration in Silicon Valley. These effects of lock in generate prohibitively high switching costs that not only deter technological diversification but also systematically deepen structural dependencies across the entire value chain.
Network effects operate through multiple channels to reinforce market concentration. Direct network effects emerge through advantages in data accumulation, where leading models benefit from expanded user bases that generate increasingly valuable training data. Simultaneously, indirect platform effects create advantages for the ecosystem that compound over time. Together, these mechanisms generate self reinforcing cycles, conceptualized as the Artificial Intelligence flywheel, that inexorably drive the market toward equilibrium structures where the winner takes all.
The theoretical foundation of this analysis draws upon and extends the concept of weaponized interdependence by integrating it with identity theory in international relations. The asymmetric structure of hubs and spokes inherently confers structural power upon hub nations, which maintain significantly lower levels of sensitivity and vulnerability compared to dependent spoke nations. This power asymmetry enables hubs to exercise control through two distinct and complementary mechanisms. Specifically, the chokepoint effect allows hubs to leverage their position at critical nodes of the network to selectively exclude or restrict access to essential resources, effectively weaponizing technological dependencies. Concurrently, the panopticon effect emerges from the privileged position of hubs in information flows, enabling comprehensive capabilities for surveillance and intelligence gathering that further enhance their strategic advantage.
The actualization of weaponization depends upon a fundamental shift in the perception of interstate identity. The transformation from a cooperative to an adversarial identity, as exemplified by the deterioration of relations between the United States and China from strategic engagement to strategic competition, serves as the primary trigger mechanism. When such shifts in identity occur, the latent potential for weaponization embedded in structures of asymmetric interdependence becomes activated, transforming technical dependencies into instruments of geopolitical coercion.
The effectiveness of this weaponization is dramatically amplified in the sector of Artificial Intelligence due to its bottleneck structure akin to the Leontief model. Unlike traditional sectors where the substitutability of inputs provides resilience, the non substitutable nature of data, human talent, and computing infrastructure means that controlling access to even a single critical element can effectively paralyze the entire innovation system of a nation. This characteristic transforms technological bottlenecks into extraordinarily potent geopolitical chokepoints, fundamentally altering the strategic calculus of international competition regarding technology. The DHCP framework thus provides an integrated analytical lens for understanding how the bottleneck structure creates conditions for asymmetric interdependence at the global level and how political factors, particularly shifts in identity and the securitization of technology, trigger the weaponization of these dependencies.

4.Key Findings (3): Determinants of Competitiveness and International Political Dynamics
The AI model innovation system exhibits distinctive systemic characteristics where Leontief-like complementarity fundamentally shapes competitive dynamics. In this structure, the weakest link among the core inputs invariably determines the overall system performance limit, creating a unique vulnerability where any single bottleneck can constrain the entire innovation capacity.
An analysis of the key input factors reveals distinct characteristics for each component of the triad. Data emerges as a foundational determinant where competitiveness fundamentally depends on the level of digitalization within a society. The creation of data moats, which refer to proprietary datasets accumulated over time, establishes formidable structural gaps between leaders and followers as these resources cannot be rapidly replicated or acquired. Human talent operates under the dynamics of superstar economics where a small cohort of elite researchers drives disproportionate innovation. This talent exhibits extreme geographic concentration, while tacit knowledge creates powerful effects of clustering. Notably, talent and computing infrastructure function as near perfect complements and form a dual bottleneck where neither factor can substitute for the other. Computing infrastructure represents perhaps the most formidable barrier and is characterized by extreme capital intensity and monopolistic market structures. The dominance of the NVIDIA CUDA ecosystem at the hardware layer and the oligopoly of hyperscalers, including AWS, Azure, and Google Cloud Platform, at the cloud layer create multiple chokepoints. Consequently, computing infrastructure is both the most potent bottleneck for innovation and the most vulnerable point for potential weaponization.
The dynamics of innovation emerge from the complex interactions among the core components and their intersection with international political forces. The synergistic interactions among these inputs generate compounding effects that transcend simple additive relationships. Specifically, the interaction between data and human talent facilitates the creation and diffusion of knowledge, where raw data is transformed into actionable insights through human expertise.
Furthermore, the interaction between human talent and computing infrastructure produces an amplification of productivity. In this dynamic, computational power exponentially enhances human research capabilities while human creativity optimizes the utilization of infrastructure. Finally, the interaction between data and computing infrastructure enables implementation at scale. Massive datasets require correspondingly massive computational resources, which creates a symbiotic relationship that drives the continuous expansion of infrastructure. These interactions collectively generate requirements for extreme capital intensity, reinforce path dependencies in technology choices, and activate the flywheel effect. This is a self reinforcing cycle where success attracts more resources, which in turn drives further success and ultimately locks in competitive advantages for early leaders.
International political dynamics fundamentally reshape these relationships. The structure of asymmetric interdependence, already solidified by path dependence and network effects, becomes a vector for strategic manipulation when international relations shift from cooperation to competition. When states undergo a transformation of identity and perceive former partners as strategic rivals, the latent vulnerabilities in technological dependencies become active instruments of statecraft.
The bottleneck structure of the sector dramatically amplifies the effectiveness of such weaponization. Because the inputs are non substitutable, controlling access to even a single critical element can cripple the entire innovation system of a nation. This transformation has profound implications for the global landscape as innovation has evolved from a primarily technical and economic endeavor into a complex international political game.
This evolution drives waves of techno protectionism and strategic decoupling. Middle powers such as South Korea find themselves in particularly precarious positions and face structural disadvantages due to a high dependency on foreign computing infrastructure and talent pools. Despite ambitious efforts toward sovereignty through initiatives such as the development of proprietary foundation models, these nations confront fundamental structural constraints that cannot be easily overcome through policy interventions alone.

5.Key Findings (4): Patent Data Analysis (267,519 Patents)
This research conducted a comprehensive quantitative analysis of 267,519 global AI algorithm and model patents spanning from 2000 to the present, employing advanced methodological approaches including text embedding techniques, unsupervised clustering algorithms, technological exploration distance calculations, revealed comparative advantage (RCA) indices, and complex network analysis. This large-scale empirical investigation provides unprecedented insights into the evolution and current state of global AI innovation patterns.
The empirical findings reveal several critical patterns in the dynamics of innovation. First, the relationship between the distance of technological exploration and competitive advantage exhibits a pronounced inverted U shaped curve. This confirms empirically that strategies of intermediate distance exploration optimize competitive advantage. This finding challenges both the conventional wisdom favoring proximate and incremental innovation as well as the rhetoric celebrating radical breakthrough innovation. Short distance exploration, while reducing risk, suffers from path dependency that limits the potential for innovation. Conversely, long distance exploration incurs prohibitive costs of coordination and confronts constraints in absorption capacity that diminish returns. The optimal strategy thus lies in carefully calibrated intermediate distance exploration that balances novelty with feasibility.
Generative AI demonstrates a distinctive dual pathway of innovation characterized by simultaneous central convergence and peripheral diffusion. The central convergence manifests in the rapid coalescence of technical development around the Transformer architecture following its introduction in 2017, establishing it as the dominant design paradigm. This convergence reflects the power of breakthrough innovations to restructure entire technological fields. Simultaneously, peripheral diffusion occurs as generative AI applications proliferate across diverse domains, from natural language processing to computer vision, from drug discovery to creative arts. This dual pathway suggests that generative AI functions both as a consolidating force that unifies disparate research streams and as a democratizing technology that enables innovation across previously disconnected fields.
In addition, the technological structure analysis reveals that generative models function as crucial mediating technology groups, serving as gateways that connect previously disparate technological domains, particularly bridging the natural language processing and computer vision axes. This mediating role positions generative models as critical infrastructure for cross-domain knowledge transfer and technological convergence. The global collaboration network has undergone a fundamental restructuring. It has shifted from a unipolar structure dominated by the United States to a dual core configuration where the United States and China function as competing gravity centers. This bifurcation reflects broader geopolitical tensions and suggests the potential emergence of parallel innovation ecosystems with limited cross pollination.
The determinants of competitive advantage have fundamentally shifted from quantitative metrics to qualitative and structural factors. Traditional indicators such as the volume of patents or the headcount of researchers show limited correlation with revealed comparative advantage. Instead, the capacity for knowledge diffusion, the diversity of the network structure, and the quality of collaboration emerge as the primary drivers of competitive advantage. This finding underscores the importance of ecosystem dynamics over the mere accumulation of resources.
Thus, distinct national innovation strategies emerge from the patent analysis. The United States maintains a balanced approach by combining steady exploration with a central positioning in global networks. China pursues rapid convergence coupled with polycentric expansion, leveraging massive research groups to simultaneously advance multiple technological fronts. South Korea, however, exhibits a path dependent strategy characterized by concentration in established domains but lacking the intermediate distance exploration necessary for breakthrough innovation. This is particularly evident in the weakness shown in the development of generative models, which is a critical limitation given the role of these models as technological gateways.

6. Conclusion
This study employed the DHCP framework to systematically demonstrate how the techno-economic bottlenecks inherent in the AI model sector undergo transformation into strategical chokepoints under conditions of strategic competition. The analysis reveals that AI innovation is fundamentally characterized by a Leontief-like bottleneck structure, wherein the non-substitutability of Data (D), Human talent (H), and Computing infrastructure (C) creates unique vulnerabilities that distinguish this sector from traditional industries. Against this backdrop this research proposes the DHCP framework, namely data, human talent, computing infrastructure, and political factors, to analyze the unique innovation characteristics and international political dynamics of the sector regarding algorithms and models.
The findings of this study demonstrate that the innovation system for Artificial Intelligence has entered a transitional phase where the logic of efficiency is being supplanted by the logic of national security. Central to this transformation is the realization that the core technological bottlenecks comprising data, human talent, and computing infrastructure are inextricably linked to international political factors. This integration facilitates the evolution of simple technical constraints into strategic chokepoints, thereby fundamentally altering the definition of a national power in the digital age and the conditions required for achieving such status. Specifically, the weaponization of these bottlenecks occurs when the international political environment shifts, turning what were once considered neutral industrial inputs into potent instruments of statecraft.
The technological and economic characteristics of the sector, namely its bottleneck structure, profound path dependency, and powerful network effects, have led to the entrenchment of a rigid hierarchical configuration. In this global landscape, a small number of hub nations have consolidated control over the ecosystem while the vast majority of states have been relegated to the status of spokes. This hierarchy is perpetuated by the extreme concentration of resources within hub nations, particularly the tacit knowledge essential for the development of frontier models as well as core computing infrastructure and high quality datasets. Under this asymmetric structure, hub nations can exercise the chokepoint effect with devastating efficiency. Because the inputs are non substitutable, a characteristic akin to the Leontief model, the restriction of access to even a single element can paralyze an entire national innovation system. This potential for the weaponization of interdependence is fully realized when interstate relations transition from strategic cooperation toward adversarial identities, rendering the traditional reliance on global supply chains for efficiency a fatal strategic vulnerability for nations excluding the United States and China.
For middle powers the meaning of being a top three power in AI must be redefined beyond mere technical rankings or quantitative growth. To achieve this status signifies the attainment of a hub nation position that serves as a primary node in global innovation networks and possesses significant bargaining power. The competition for foundation models has reached a point where the middle ground has disappeared, necessitating a fundamental shift from efficiency centric strategies toward those prioritizing strategic autonomy and the construction of an attractive innovation environment. The essential conditions for this advancement comprise four critical pillars, namely the possession of globally competitive foundation models, the attainment of strategic autonomy over core inputs through alternative pathways, the maintenance of a sustainable innovation ecosystem through a virtuous cycle of investment, and the acquisition of a significant voice in global governance regarding the formation of norms.
In particular, the possession of independent development experience for foundation models and the maintenance of a full stack capability encompassing semiconductors, cloud infrastructure, and services serves as vital diplomatic leverage. Such capabilities provide the foundation for resilience by enabling the identification of alternative routes when strategic chokepoints are activated. Furthermore, the sustainability of elite human talent who possess undocumented tacit knowledge represents the most critical factor for long term competitiveness. A fundamental strategic pivot is required to move beyond simple financial incentives toward the creation of a professional stage and specific roles where talent can perform frontier research. The current structural deficiency in many domestic ecosystems is not a matter of salary but rather the absence of the advanced modeling and architectural design positions that elite engineers require. Without the creation of these specialized roles, the influx of talent remains impossible regardless of financial compensation.
The concept of sovereign AI should be reconceptualized not as a closed form of self sufficiency but as the attainment of strategic autonomy and resilience within the global ecosystem. This requires an integrated strategy that recognizes the systemic interdependencies among the core inputs and demands a holistic policy approach. By establishing an attractive innovation environment based on the soft power of a nation, middle powers can provide a psychological and institutional safe zone for researchers. Ultimately, the ability to manage the international political environment while ensuring the continuous flow of data, talent, and computing power will determine whether a nation can successfully navigate the transition from a dependent spoke to an indispensable and resilient hub in the global hierarchy of AI innovation system.
Therefore, the study argues that the attainment of hub status depends on the ability to manage geopolitical risks while securing control over the essential triad of data, human talent, and computing infrastructure.

국문 요약 i

영문 요약 I

제1장 서론 1
제1절 연구배경 및 연구목적 1
제2절 선행연구 분석 및 연구질문 3
제3절 연구범위 및 연구방법 6

제2장 기술 산업 부문에서 나타나는 혁신과 안보 분석 프레임워크 8
제1절 AI 모델의 기술발전 궤적과 혁신 경쟁 패러다임 변화 8
제2절 AI 시스템의 혁신 생태계 : 지식의 생산과 확산 구조와 안보위협요인 16
제3절 대체가능성에서 레온티예프적 상호보완성으로 : AI 모델 성능 개발 투입요소의 특수성 21
1. 생산함수의 기본 개념과 투입요소 간 대체가능성 21
2. 주요 생산함수 모형 22
3. AI 모델 부문에서의 대체 불가능성과 레온티에프적 병목 구조 24
제4절 AI 산업 혁신생태계 분석 프레임워크: AI 모델 혁신의 핵심 투입요소와상호의존의 무기화 메커니즘 27
1. 전통적 혁신이론의 한계와 새로운 프레임워크의 필요성 28
2. AI 모델 부문에서의 비대칭 상호의존 구조와 상호의존의 무기화 30
3. AI 모델 혁신 동학의 통합 분석 프레임워크 (DHCP 프레임워크) 35

제3장 AI 모델 경쟁력의 결정요인과 국제정치 동학 : 데이터, 인재,컴퓨팅인프라의 레온티예프적 병목과 상호의존의 무기화 38
제1절 AI의 혁신특성: 모델 개발 및 경쟁력의 결정요인을 중심으로 39
1. 데이터 40
2. 인재 42
3. 컴퓨팅 인프라 46
제2절 AI 모델 산업(sector)에서의 혁신 다이나믹스: 기술･경제적 병목에서 안보적 초크포인트로 51
1. 시스템으로서 AI 모델과 데이터, 인재, 컴퓨팅 인프라의 상호작용 51
2. AI 모델의 혁신 특성과 상호의존의 무기화 효과성 58
제3절 소결 63

제4장 AI 알고리즘 및 모델 부문 특허 데이터 분석 65
제1절 데이터 분석 및 방법 65
1. 데이터 수집 및 구축 65
2. 분석 방법 78
3. 분석 방법 요약 89
제2절 기술구조와 경쟁우위 분석 91
1. 기술 클러스터 분석 결과 91
2. 기술 탐색과 경쟁우위 분석 101
3. 계량 분석 109
4. 네트워크 분석 119
5. 혁신 경로 분석 125
제3절 소결 133

제5장 결론 136
제1절 요약 136
제2절 전략적 방향성 및 정책 제언 138

참고문헌 147

부 록 159

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AI 산업 혁신특성 분석 연구 - AI알고리즘 및 모델 부문을 중심으로 -

An Analysis of Innovation Characteristics in the AI Industry: Focusing on the AI Algorithm and Model Sector

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