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TODAY'S LEAD STORY

China Leads the Robotaxi Race — New Scorecard Reveals the 'Geopolitics' of Autonomous Driving

The latest robotaxi scorecard highlights China's overwhelming dominance in the sector. While Waymo leads in the U.S. market, multiple Chinese companies are accelerating their commercial deployments, bringing the geopolitical landscape of autonomous driving into sharp focus.

China Leads the Robotaxi Race — New Scorecard Reveals the 'Geopolitics' of Autonomous Driving
(Photo: illustrative)

The "Scorecard" That Exposed the Reality of Self-Driving Cars

As someone who spent many years as an engineer in Silicon Valley, I can't think of any technology that has been hyped as "just around the corner" longer than autonomous driving. Google launched its self-driving car project in 2009. Seventeen years have passed since then, and the shape of the industry has finally come into focus. What this week's newly released robotaxi scorecard revealed, however, is that the shape looks nothing like what anyone expected.

According to the latest robotaxi scorecard reported by TechCrunch Mobility, Chinese players have pulled significantly ahead of their American counterparts across every metric — commercial deployment scale, geographic coverage, and fleet size.

China Dominates on "Volume"

In the U.S. market, the conversation is essentially limited to Waymo. The Alphabet subsidiary operates paid services in San Francisco, Phoenix, and Los Angeles, and continues to receive high marks for technical sophistication. But measure it against "commercial scale," and the picture changes considerably.

In China, multiple players — Baidu's Apollo Go, DiDi, Pony.ai, and WeRide — are simultaneously expanding services across major metropolitan areas including Beijing, Shanghai, Guangzhou, and Shenzhen. Whether measured by service area coverage, fleet vehicle count, or total miles driven, Chinese companies are steadily accumulating numbers, and their advantage on the scorecard is becoming increasingly visible in concrete terms.

"Speed of Regulation" as a Competitive Factor

One variable that cannot be overlooked here is the regulatory environment. In China, local governments have been aggressively expanding designated zones for autonomous driving trials, and the issuance of commercial operating licenses has accelerated. Beijing has reportedly approved fully driverless taxi commercial operations across the entire city as of the end of 2025, and the pace of legal frameworks has been actively enabling industry growth.

In the United States, by contrast, federal and state-level regulations are intricately layered, and expanding into new areas still involves lengthy review processes. While Waymo continues to grow steadily, the pace of that growth is fundamentally different in nature from the kind of scaling happening in China's major cities.

Depth of Technology vs. Breadth of Deployment

A debate frequently heard among industry insiders goes something like this: "Waymo's AI is world-class, but Chinese players are accumulating training data through sheer scale." Driving data is essential for improving autonomous AI performance. Building the ability to handle edge cases — unexpected situations — requires vast amounts of real-world driving data across diverse road environments. In this respect, there is a potential feedback loop at play: "Chinese players with larger fleets are at an inherent advantage."

Pony.ai completed its NASDAQ listing at the end of 2024, and WeRide has been stepping up its fundraising in the U.S. market. It is clear that Chinese robotaxi companies are not content with domestic expansion alone — they are actively seeking to grow their presence in global capital markets and in the shaping of technology standards.

Ripple Effects for Japan and Asia

This trend is far from irrelevant to Japan. While Japanese automakers such as Toyota and Honda continue to pour substantial investment into autonomous driving, commercial robotaxi services in urban areas remain behind those in Europe, the United States, and China. Chinese players are already beginning to enter Southeast Asian markets, leveraging their technology and deployment track records, and the geopolitics of mobility across Asia as a whole is on the verge of being redrawn.

Conclusion: It's Not "Who Can Drive Faster," but "Who Has Driven More"

The autonomous driving race has shifted from a purely technological contest to an industrial competition defined by who can achieve commercial scale fastest and accumulate the most data and trust. This scorecard lays out that reality in hard numbers. While Waymo continues to pursue technical refinement, Chinese players are out there actually driving on city streets. How the U.S. and Japanese industries respond — before that gap becomes an insurmountable advantage — will be one of the defining questions of the next several years.

自動運転ロボタクシー中国テックモビリティスタートアップ

US Robotics Industry Achieves Double-Digit Growth in 2025 — IFR Report Reveals New Trend of 'Shift to Non-Manufacturing'

According to the latest IFR report, the US robotics industry achieved double-digit growth in 2025. The growth is being driven by non-manufacturing sectors such as the food industry, signaling a structural shift away from the traditionally dominant automotive and electronics sectors.

The Era of "Manufacturing Only" Is Over

A recent report published by the International Federation of Robotics (IFR) clearly illustrates a seismic shift in the robotics industry. The U.S. robotics sector achieved double-digit growth in 2025. The numbers themselves are impressive enough, but what I find more compelling is what lies beneath the surface.

The growth was driven by non-manufacturing sectors, led by the food industry. In other words, rather than automotive or electrical and electronics — once considered the primary battleground for robotics — fields such as food processing, logistics, and services are becoming the new pillars of robot adoption.

Why "Non-Manufacturing"?

Anyone inside the robotics industry probably sensed this intuitively, but IFR's data has turned that intuition into conviction.

Multiple factors are intertwined here. First, the worsening labor shortage. Food processing and logistics involve a great deal of physically demanding, dirty, and hazardous work, making it structurally difficult to secure workers. Just as the U.S. Navy's industrial base review estimated a need for 174,000 new workers (as noted by GrayMatter Robotics), similar issues are surfacing in defense and manufacturing environments. This connects directly to the growing demand for robotic automation of tasks such as autonomous surface finishing and grinding.

Second, the "democratization" of robots themselves has progressed. Advances in AI, sensors, and control technology have allowed robots that were once limited to high-precision automotive assembly lines to adapt far more flexibly to diverse environments. The spread of collaborative robots (cobots) is a clear symbol of this, but it goes beyond that — the usability of control software has also improved dramatically.

"Expanding Range of Motion" — The Next Move

Around the same time this data emerged, Güdel made an interesting move. The company plans to exhibit at Automate 2026 with a system called TMV/TMF, which adds vertical and horizontal motion capabilities to its heavy-duty grinding robots.

This may look incremental, but I believe it represents a fundamental evolution. It means robots can now move themselves to handle large workpieces, going beyond the limits of what stationary robots can reach. This directly connects to applications involving large workpieces — such as the expansive floors of food processing plants, or the surface finishing of ship hulls and aircraft fuselages. When viewed through the lens of expansion into non-manufacturing sectors, the dots start connecting into a line.

Reading "Double-Digit Growth" Through a Software Lens

As a former engineer, whenever market statistics like these come out, my first thought is: "So, how does this change the software?"

The acceleration of expansion into non-manufacturing means the diversity of environments in which robots operate will increase explosively. Wet environments in food factories, dynamic obstacles in logistics warehouses, irregularly shaped workpieces in defense facilities — conventional fixed-program approaches have their limits when it comes to handling all of these. The growing attention to physical AI and agentic AI is also a natural extension of this same backdrop.

The message from GrayMatter Robotics — that "autonomous surface finishing determines defense industry readiness" — is not mere marketing talk either. Read in this context, it carries a much more tangible weight.

In Closing: Time to Update the "Robots = Factory" Assumption

The IFR's double-digit growth news is not just a story about economic recovery. It is a signal that the very places where robots operate are being redefined.

Food, logistics, defense, construction — demand for the controllers, software, and sensors needed to deploy robots in these non-manufacturing environments will only continue to grow. Rather than simply tracking market growth, I believe the answers to the question of "where, and to do what?" will be what shapes the next wave of industrial design.

ロボット産業IFR市場動向非製造業食品ロボット

What is the 'Control Roadmap'? — DeepMind's Blueprint for AI Agent Internal Controls

DeepMind's published 'AI Control Roadmap' is a framework for safely operating internal AI agents. This article explains the full picture of a multi-layered defense architecture that combines traditional safeguards with real-time monitoring.

The Era When AI Agents Become "Internal Infrastructure"

In 2026, AI agents are no longer products of the laboratory. Systems that write code, send emails, search internal databases, and select their next actions without human approval have begun to integrate themselves into the internal systems of companies around the world.

This shift raises one essential question: "Who watches the agents?"

In June 2026, DeepMind offered its own answer to that question by publishing the "AI Control Roadmap" — a comprehensive governance framework for safely operating the fleet of AI agents deployed within the company.

"Trust Alone Is Not Enough" — The Philosophy of Defense in Depth

At the heart of the roadmap lies the recognition that simply "trusting the model" is insufficient. No matter how capable a model may be, it can exhibit unintended behavior when confronted with unexpected inputs, prompt injection attacks, or situations its designers never anticipated.

DeepMind's approach rests on two major pillars.

The First Pillar: Inheriting and Strengthening Conventional Safeguards This means applying to AI agents the techniques long established in software security — access controls, least privilege, and logging. The design explicitly defines the scope of resources an agent can manipulate and places "circuit breakers" throughout the system at which human operators can intervene.

The Second Pillar: Introducing a Real-Time Monitoring Layer This is where the approach diverges sharply from conventional software security. The behavior of AI agents cannot be fully captured by a static ruleset alone. DeepMind seeks to close the "gaps" in static rules by layering multiple monitoring systems that observe an agent's action stream in real time and detect anomalous patterns.

The "Three Phases" Outlined in the Roadmap

What DeepMind's blog makes explicit is not a single technology, but a phased deployment roadmap.

In the first phase, agent actions are restricted to those that are "reversible." Reading data and generating suggestions are permitted, but writing to external APIs or making significant configuration changes requires human approval. It is, in effect, a gradual expansion of privileges beginning from a "read-only mode."

In the next phase, the reliability of the monitoring systems themselves is verified. Patterns by which agents might attempt to evade oversight — known as "sandbagging" or "deceiving the monitor" — are actively tested, and the robustness of the system is continuously evaluated.

In the final phase, the goal is to implement a mechanism that dynamically adjusts the balance between the cost of human intervention and the degree of autonomy. The idea is that as an agent accumulates a track record of trustworthy behavior, the approval process becomes progressively streamlined.

Why Publish This Framework "Now"?

What deserves attention is that DeepMind chose not to keep this for internal use, but to release it publicly. The backdrop is a situation in which no industry-wide standard exists for the internal governance of AI agents.

In effect, the company is presenting this roadmap as a starting point for an industry standard. By publishing its design ahead of others, Google's DeepMind appears to be aiming to create a "common language" that competitors and regulators alike can reference.

As the capabilities of AI agents expand at a rapid pace, the competition is shifting from "what they can do" to "how to control them" — DeepMind's roadmap is a document that symbolizes that transition.

Conclusion

DeepMind's "AI Control Roadmap" is significant in that it brings a concrete blueprint to the conversation around the internal governance of AI agents. The fusion of conventional safeguards with real-time monitoring, and the three-phase structure of gradual privilege expansion, will serve as a practical reference for other companies designing their own AI agent governance systems. As agents evolve from "tools that are used" to "entities that work," designing a control architecture is becoming an unavoidable management challenge.

DeepMindAIエージェントAI安全性セキュリティコントロールロードマップ
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