We are witnessing a structural convergence. AI agents, Physical AI, and the governance frameworks required to secure them are no longer the domain of a few pioneering companies; they are central drivers of competitive advantage, industrial reorganization, and global capital flow. 

The leadership team at NEC X works daily with its portfolio companies to build some of the most promising purpose-built AI solutions, from software that accelerates enterprise workflows to the first smart cane for the visually impaired. Here’s what they see unfolding in 2026. 

AI Delegation, Integration and the Rise of Japanese Startups 

Shintaro Matsumoto / CEO & President  

By  2026, the presence of AI agents will be a given, and the real source of advantage will shift to how they are designed, where they are deployed, and how far decision-making is delegated to them. As AI agents become embedded in organizational and societal decision processes, the technical importance of security, governance, and identity management, including credential management, will rise sharply. 

A key challenge for enterprise AI will be how far human implicit knowledge and on-the-ground expertise can be modeled, which will increasingly determine success or failure. Along this trajectory, advances in simulation and sensing will drive serious progress in the social implementation of Physical AI. 

At the same time, from the perspective of capital flows, Japan’s market is gaining renewed importance. Against a backdrop of geopolitical risk, Japan is reemerging as a destination for global capital, and many global VCs are showing strong interest in Japan-born startups that can scale globally. 

AI Agents as Economic Actors

Naoto Mizuguchi / CFO

AI agents are beginning to evolve into entities that effectively exhibit a “personality.” Previously  limited to simple, narrowly defined tasks, AI agents are starting to be introduced as “AI employees” inside organizations and, in cybersecurity, are even taking on deceptive behaviors that are treated as if they belonged to a single actor.​ 

As AI agents start to behave as economic actors, their credential management will become a major issue. Similar to human employees, there will be growing demand for new governance and security architectures that cover permissioning, lifecycle management of credentials, and protection against impersonation and abuse.​ 

In parallel, the surrounding technologies are rapidly advancing. More accurate simulation models and more sophisticated sensing will likely accelerate the progress of Physical AI, and while the mass adoption of humanoids in 2026 is uncertain, AI agents operating in the physical world will steadily gain real presence. 

The Shifting Landscape of Gen AI for Scientific Research and Robotics 

Nobu Morita / Director of Innovations 

Generative AI dramatically boosted productivity in creative and office work in 2025, and it will remain the biggest focus area in 2026. The next visible wave will be the acceleration of R&D in scientific domains such as drug discovery, materials, agriculture, and energy, where AI-led research is already producing startups that achieve unicorn valuations from their first financing rounds.​ 

Even as concerns about a bubble grow, there is a prevailing view that one or two winners in each segment will continue to scale significantly, driving sustained capital inflows as investors compete to back the eventual leaders. Alongside this, advances in AI will push robotics and Physical AI further into real-world deployment, with services like Waymo’s robotaxis already functioning as infrastructure in the Bay Area and making a car-free lifestyle increasingly realistic.​ 

The commercialization of humanoid robots is expected to begin in 2026, with the initial impact coming from the automation of relatively simple tasks in complex environments, such as moving people, which will still have major societal effects. Defense and space will also be critical, and if IPOs by companies like SpaceX or Anduril materialize, they could further energize these domains both financially and in terms of visibility.​ 

From a more unconventional angle, Japan’s position is notable because, unlike much of the world, it has maintained low interest rates and lower capital costs. One viable strategy is to raise capital in Japan and acquire overseas companies whose valuations have compressed and that are struggling to fundraise, using them as a springboard for global expansion. But with rate regimes already at a turning point, this window will not stay open for long, making speed decisive. 

Early-Stage AI Investment Challenges and Opportunities

Ryo Kaneko / Director of Innovations

For early-stage US startup investment in 2026, the evolution of AI agents may paradoxically make this a testing year for many teams. As AI begins to be deployed at the level of end-to-end workflows, traditional vertical SaaS-style AI risks being seen as “not as flexible as in-house builds yet too generic as an off-the-shelf product,” especially given the cost of customization for unstructured work.​ 

Sophisticated agent designs using technologies like MultiCore Processing may offer a path forward, but balancing scalability and generality will be difficult, while horizontal categories such as Enterprise Resource Planning and Customer Relationship Management will remain highly competitive with limited room for new entrants. In this environment, areas with real disruptive potential in 2026 include Operational Technology/AI platforms supporting Manufacturing Execution Systems and quality control in manufacturing, semi-custom robots combining generic components with automatic code generation, small and modular satellite manufacturing platforms, drone services riding deregulation tailwinds, and micro-payment services designed for agent-to-agent transactions.​ 

What these opportunities share is the need to deliver value across physical, operational, and regulatory dimensions. Founders in these spaces will need deep, experience-based insight as well as strong technical capabilities to succeed. 

Multi-Agent Systems and Physical AI

Takayuki Kihara / Director of Innovations

One key shift is the evolution of agentic AI from task-specific agents into multi-agent systems where multiple agents specialize and collaborate. While single-function agents are already being introduced to streamline specific tasks, the mainstream is moving toward designs with an orchestration layer that can coordinate cross-functional and cross-organizational workflows and decisions.​ 

At the same time, organizations will become more selective about where to apply agentic AI, with ROI and implementation complexity driving scrutiny. The ability to move beyond PoCs and prove value across interdepartmental coordination and entire decision-making processes will be crucial to scaling.​ 

The second shift is the “agentization” of the physical world enabled by Physical AI. Robotics is moving beyond the experimental phase into a stage where use cases are being narrowed and scaled, supported by advances in GPUs and lightweight VLMs running at the edge that allow AI to perceive, decide, and act directly in the field.​ 

In domains such as factories, logistics, and construction, deployments are starting to move from demos and pilots toward real operational rollout. In this sense, 2026 is poised to be the year when agentic AI begins a transition from being a tool to becoming a true economic actor.