JAPAN Trends and Developments Contributed by: Haseru Roku and Yoshiteru Matsuzaki, Nagashima Ohno & Tsunematsu
Introduction Japan’s technology M&A is reshaping how traditional industries –ie, manufacturing and automotive – imple- ment AI and digital transformation. Acquisitions and partnerships span the entire value chain, from semi- conductors through applications to physical AI imple- mentation, while concurrent M&A activity focuses on IT talent acquisition, addressing Japan’s persistent workforce shortage. M&A success requires integrating organisational capability with technological consoli- dation – fusing hardware-centric corporate cultures with agile software methodologies while consolidat- ing technology platforms. Japan’s acquisition-driven talent strategy contrasts sharply with US technology sectors that achieve efficiency through workforce reductions, reflecting structural differences in labour market dynamics and the IT talent pipeline. These dis- tinctive market characteristics mean that regulatory compliance, post-acquisition cultural integration and workforce retention will likely determine competitive advantage. Investment Approaches Across the AI Value Chain Japanese enterprises pursue AI capabilities through diverse transaction structures – acquisitions, strate- gic investments and business partnerships. These investments follow distinct patterns depending on their position within the AI value chain and the stage of development. Technical architecture: four-layer value chain The Japanese AI investment landscape comprises four distinct technical layers: • layer 1 provides foundational AI chip hardware; • layer 2 delivers computational capacity infrastruc- ture (data centres and cloud platforms); • layer 3 encompasses foundation models (large lan- guage models and multimodal AI systems); and • layer 4 includes AI applications – vertical AI, AI agents for business process automation and physi- cal/embedded AI systems. Transaction patterns: infrastructure versus application investments From a transaction structure perspective, these four technical layers resolve into distinct investment pat- terns. Infrastructure layers (1–3) emphasise strategic
control, capital deployment and established technol- ogy pathways; the application layer (4) demonstrates partnership-dominant approaches, though market positioning creates strategic variations. Infrastructure-orientated layers: strategic control and co-ordinated development Layers 1–3 (AI chips, computational capacity, foun- dation models) share three characteristics that help explain control-oriented transaction structures: • strategic control imperative – ownership or exclu- sive access to foundational capabilities signifi- cantly influences competitive positioning across all AI-dependent industries; • capital intensity – development requires long-term commitments beyond typical partnership horizons; and • established development pathways – proven technology trajectories justify major capital deploy- ment. These characteristics help explain why Japanese investments in infrastructure layers tend to con- centrate on control-oriented transaction structures – including acquisitions, strategic investments and co-ordinated public-private initiatives (ie, joint pro- grammes between government and private compa- nies) – rather than partnership-based approaches. Layer 1: AI chips and semiconductors AI-specific processors, graphics processing units (GPUs) and neural processing units (NPUs) constitute the foundational hardware enabling AI deployment at scale. Japanese investments reflect the recognition of chip capabilities as strategic national infrastructure requiring co-ordinated public-private approaches. In December 2024, Preferred Networks – led by SBI Group – raised JPY19 billion to develop ultra-low- power AI processors and foundation model capabili- ties. Complementing this venture approach, in Janu- ary 2025 Mitsubishi Corporation, Preferred Networks and Internet Initiative Japan established the Preferred Computing Infrastructure joint venture to commercial- ise secure AI infrastructure, exemplifying vertical inte- gration from AI processor architecture through secure infrastructure delivery.
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