Introduction.
During the week of November 9-15, 2025, the global AI industry reached a historic turning point: a strategic alliance between Apple and Google, major infrastructure investments by NVIDIA and OpenAI, a dramatic increase in memory prices by Samsung, and a breakthrough in AI medical diagnostics once again demonstrated the power of AI technology to reshape the very structure of industry itself.
This paper reviews the major AI news during this period and discusses how Japanese manufacturers in particular should strategically view and apply these trends to maintain and enhance their competitiveness AI is no longer a technology trend for a specific IT department, but a paradigm shift that will force company-wide management change It is necessary to
1. major global AI news and implications for manufacturing
1.1. Apple-Google Alliance: Siri Revolution with 1.2 trillion parameters
Most notable this week was Apple’s strategic partnership to leverage Google’s 1.2 trillion-parameter “Gemini” AI model for Siri, for which Apple will pay approximately $1 billion per year, with a targeted launch in spring 2026.
Potential applications in the manufacturing industry
The partnership signals an acceleration in the practical application of multimodal AI, where trillion-parameter models can process speech, text, image, and environmental data in an integrated manner.
- Upgrading of work instructions and knowledge transfer: Simply by having the operator make a complex inquiry by voice, AI instantly refers to drawings, manuals, and past trouble cases to convey the most appropriate procedures and know-how. Knowledge transfer can be accelerated by recording the know-how of veteran technicians in voice and video, and having AI understand the context and pass it on to younger workers.
- Efficient inspection and quality control: Combining image recognition and natural language processing, the system automates not only the detection of defective products, but also the analysis of their causes.
- Implication: As UI/UX oriented companies like Apple continue to “democratize” AI, an environment is being created where advanced AI can be used without expertise in the manufacturing industry.
1.2. NVIDIA-OpenAI 10 Gigawatt Collaboration: Infrastructure Revolution in the AI Industry
NVIDIA and OpenAI are in full swing with plans to invest up to $100 billion to build at least 10 gigawatts (GW) of AI data centers with NVIDIA systems (announced in September 2025). This is a huge project with millions of Vera Rubin GPUs being deployed.
Implications for Manufacturing
The scale of 10 GW is equivalent to 10 medium-sized nuclear power plants, indicating the reality that AI is becoming an energy industry.
- Supply Chain Impact: Demand for semiconductors, power equipment, and cooling systems is expected to surge, with spillover effects on construction and heavy electrical equipment manufacturers. Pressure for sustainable energy supplies will further increase.
- Economics of AI use: the cost of accessing large-scale language models (LLMs) will be reduced in the long run, and the performance of cloud-based AI services will improve, making advanced AI analytics potentially affordable and accessible to small and medium-sized manufacturing companies.
- Changing competitive landscape: Access to AI infrastructure will become a source of competitive advantage, making the strategic choice between owning your own data center or using cloud services important. Power costs may become a new factor in selecting production locations.
1.3. Samsung Memory Price Increase of 60%: Impact of AI Special Demand
Samsung Electronics Korea raised DDR5-standard DRAM prices by up to 60% compared to September due to supply shortages caused by the rush to build AI data centers.
Manufacturing Impact Analysis
Soaring memory prices will bring direct cost pressure to the digitization and AI of manufacturing.
- Increased cost pressure: IoT sensors, edge devices, and industrial computers will increase in cost, leading to higher development and manufacturing costs for AI-powered products.
- Re-examine investment strategy:.
- Increasing attention is being paid to “lightweight AI ” (AI that achieves maximum effectiveness with minimum memory requirements).
- The increasing dominance of edge AI technology (processing completed on the device side) will make diversification and risk diversification of the AI semiconductor supply chain essential.
1.4. A Leap Forward in AI Medical Diagnostics: Achieving Accuracy Beyond Humans
AI medical diagnostic systems can now detect everything from cancer to rare genetic diseases with speed and accuracy surpassing human doctors, and on November 12, a new AI platform, AI.VALI, was announced that will redefine early detection of disease.
Development of applications in the manufacturing industry
Advanced AI technology proven in medical diagnosis can be directly applied to quality control and predictive maintenance in the manufacturing industry.
- Advanced Quality Inspection: X-ray and CT image analysis technology is applied to product inspection to detect minute defects and abnormalities at an early stage (equivalent to “detection before symptoms appear” in the medical field).
- Improved predictive maintenance accuracy: Real-time diagnosis of equipment “health” and detection of signs of failure weeks or months in advance to optimize maintenance planning and minimize downtime.
- Data-driven decision making: like “AI Soft Sensor” launched by NTT Docomo Business, it reduces defect rates by discovering “invisible patterns” from vast amounts of sensor data and building predictive models for quality problems.
1.5. AI Automation and the Future of Employment: Impact on 3 Million People
According to Goldman Sachs projections, AI automation could affect 300 million equivalent full-time jobs worldwide.
Implications for Manufacturing Human Resource Strategies
Rather than taking this forecast pessimistically, we should use it as an opportunity for strategic redeployment of human resources. As Japan’s manufacturing industry faces a serious labor shortage due to the declining birthrate and aging population, AI should be positioned as an indispensable partner that “maintains productivity” rather than “takes away jobs.
- Accelerate skill shift: A company-wide development of data literacy and AI utilization skills is required to encourage a shift from routine tasks to AI monitoring and analysis work.
- Building collaborative models between humans and AI: It is important to practice “augmented intelligence” (augmented intelligence) that combines AI’s “pattern recognition and mass data processing” capabilities with human “creativity and flexible judgment.
- Creation of new employment opportunities: Increased demand for personnel specializing in AI implementation and operations, “AI strategists” who translate AI-generated insights into strategy, etc.
1.6. Accelerating Corporate Adoption of Generative AI: The Realities of Organizational Transformation
The rate of corporate adoption of generative AI is rapidly increasing and is beginning to transform organizational structures and work styles beyond operational efficiency.
Examples of Generative AI Applications in the Manufacturing Industry
- Optimize production planning: Demand forecasting, inventory management, and supply chain coordination are analyzed in an integrated manner to automatically generate optimal plans that take into account multiple constraints.
- Streamlining design and development: AI generates initial drafts of product designs, which are then refined by engineers to shorten development time.
- Automatic creation of manuals and procedures: Automatic generation of documents that explain work processes in natural language and expedite multilingual support.
- Implication: AI is evolving as a “supporter” rather than a “replacement” for people, as in Softbank’s “X-Ghost”; it is essential to foster a culture that views AI as an empowerment tool rather than a threat.
1.7. Trends in Japanese Companies: NTT and Renesas
- NTT’s urban development demonstration experiment : NTT West launched an urban development demonstration experiment using the “collective knowledge generation by AI” technology developed at the Expo. This technology can be applied to supply chain management and complex production coordination in the manufacturing industry.
- Renesas Semiconductor for AI Memory: Renesas Electronics has developed a semiconductor for AI memory that has been adopted by Samsung. This shows that the Japanese semiconductor industry is regaining its competitive edge in developing products for the AI age.
AI Application to Manufacturing: Five Strategic Recommendations
Trends over the past week have demanded concrete action from the manufacturing sector.
2.1. Develop a roadmap for phased AI implementation
We will introduce the system in stages, starting with areas where results are easy to see, and expand while verifying ROI (return on investment).
- Phase 1 (short term: 6 months to 1 year): Automated quality inspection using image recognition, document creation and translation using generated AI, and predictive detection using AI analysis of existing sensor data.
- Phase 2 (mid-term: 1-2 years): Full-scale implementation of predictive maintenance system, deployment of AI for production planning optimization, and start of digital twin construction.
- Phase 3 (long term: 2-3 years): AI-driven supply chain integration, partial implementation of autonomous control systems, and company-wide deployment of AI design support.
2.2. Focus on “Lightweight AI” and “Edge AI
Invest in resource-efficient technologies to address rising memory prices and increasing power costs.
- Leverage small AI models: Leverage small AI models (hundreds of MB to several GB) running on-device, and use edge AI for the parts that require real-time processing.
- Model “distillation”: leverages techniques to transfer knowledge from larger models to smaller models, reducing costs and resources while maintaining high performance.
2.3. Restructuring of data strategy
AI performance is determined by “data quality and quantity.
- Data integration and standardization: Integrate data scattered throughout the company and standardize it into a form that is easy for AI to learn.
- Thorough “visualization”: Promote thorough data collection by adding more sensors.
- Combination with external data: Combine with external data such as weather, market trends, etc. to improve forecast accuracy.
2.4. Human Resource Development and Organizational Culture Change
More important than the introduction of technology is “people.
- AI literacy training: Training for all employees to understand what AI can and cannot do.
- Knowledge Engineering: Develops personnel to incorporate the knowledge of veteran engineers into AI systems.
- Foster a culture of experimentation: Recognizing that the introduction of AI is based on trial and error, management will commit to and foster a “culture of experimentation without fear of failure.
2.5. Ecosystem building and open innovation
We will move away from a self-focused approach and utilize outside wisdom.
- Collaboration with AI startups: Collaborate with flexible and specialized AI startups to rapidly incorporate technology.
- Participation in industry standardization activities: Participate in industry standardization activities for data formats, APIs, etc., and promote AI throughout the value chain involving suppliers and customers.
Conclusion: The Future of Manufacturing in the AI Era
AI-related news in November 2025 clearly indicates that the evolution of the technology is moving from the “experimental phase” to the “social implementation phase”: the Apple-Google partnership symbolizes the democratization of advanced AI, the NVIDIA-OpenAI collaboration is a full-scale industrialization of AI infrastructure, and Samsung’s price increase is symbolizes the structural change in supply and demand, respectively.
For manufacturers, this is both a crisis and a once-in-a-lifetime opportunity: AI can be the answer to the challenges facing Japanese manufacturers, including labor shortages, increased global competition, and supply chain complexity.
The key is to position AI implementation not as a “technology project” but as a “management transformation. Only with commitment from the top, company-wide digital literacy, a data-driven culture, and collaboration with the ecosystem will AI provide a true competitive advantage.
In 2025, the AI revolution is a reality that cannot wait. Now is the time for manufacturers to take strategic action to win the next decade.
Source List
- ETC Journal – “Three Biggest AI Stories in Nov. 2025: ‘AI is no longer siloed'” (Nov. 13, 2025)
- Tech AI Magazine – “AI Daily Digest for 09 November 2025” (November 9, 2025)
- Bloomberg – “Apple Plans to Use 1.2 Trillion Parameter Google Gemini Model to Power New Siri” (November 5, 2025)
- Reuters – “Apple to use Google’s AI model to run new Siri” (November 5, 2025)
- TechCrunch – “Apple nears deal to pay Google $1B annually to power new Siri” (November 5, 2025)
- NVIDIA News – “OpenAI and NVIDIA Announce Strategic Partnership to Deploy 10GW of NVIDIA Systems” (September 22, 2025).
- OpenAI – “OpenAI and NVIDIA announce strategic partnership to deploy 10 gigawatts” (September 22, 2025).
- Reuters – “Samsung to raise memory prices by up to 60%, driven by AI demand” (November 14, 2025)
- Chosun Biz – “Samsung raises some DRAM prices by 60%, causing panic buying due to memory shortage” (November 14, 2025).
- Phoenix BioScience Core – “AI.VALI’s Breakthrough Technology Brings Early Detection Into a New Era” (November 2025) November 12, 2025)
- Fortune – “How fewer jobs and cultural backlash create a governance crisis” (November 8, 2025)
- Goldman Sachs – “Generative AI could raise global GDP by 7%” (April 5, 2023)
- McKinsey – “The State of AI: Global Survey 2025” (November 5, 2025)
- CIO.com – “Corporate Adoption and Use of Generative AI: The Realities and Future of Organizational Transformation as Seen from the Field.”
- NTT West – “NTT West to Launch Demonstration Experiment for Urban Development Utilizing ‘AI-based Collective Knowledge Generation’ Technology and Knowledge Developed at Osaka/Kansai Expo” (November 14, 2025)
- Softbank – “AI to Support, Not Replace, People. X-Ghost, an autonomous thinking AI operator for contact centers, has been launched” (Nov 13, 2025)
- NTT – “Launch of “AI Soft Sensor” introduction support package to accelerate field-led AI utilization” (November 12, 2025)
- Nikkei – “Renesas Electronics Develops Semiconductors for AI Memory, Samsung Adopts Them” (November 12, 2025)
- ExaWizards – “13 AI/Generative AI Applications in Manufacturing [2025 Edition]” (October 27, 2025).
- Smart Factory Institute – “[2025] What is Predictive Maintenance? Background to the focus of attention and benefits of introduction” (November 4, 2025)
