Introduction.
The evolution of the artificial intelligence (AI) field continued unabated during the fourth week of 202510 (October 19-25), with the intensifying semiconductor race between Nvidia and AMD, the announcement of Amazon’s new AI robot “Blue Jay,” and the official release of OpenAI’s “Sora 2.” It was a week full of noteworthy topics that will affect the future of manufacturing. This paper picks up the week’s major AI news stories and takes an in-depth look at how the Japanese manufacturing industry, in particular, can take advantage of this technology and its potential applications.
1. trends in the semiconductor and infrastructure sectors
Rise of Nvidia and AMD
Nvidia CEO Jensen Huang declared that the United States is entering a “new AI-driven industrial revolution. He credited the Trump-era tariff and energy policies for accelerating domestic semiconductor manufacturing and unveiled the first US-made Blackwell wafers. He stressed that $500 billion in AI infrastructure investment is expected over the next four years, and that the development of skilled engineers is essential to support this growth.
Meanwhile, OpenAI’s partnership with AMD is worth tens of billions of dollars, and AMD will build OpenAI a 6-gigawatt scale AI infrastructure with AMD chips. Notably, OpenAI has the right to purchase up to 160 million shares of AMD stock at 1 cent per share and could own approximately 10% of AMD when the milestone is reached. Following the announcement, AMD stock rose 34%, increasing its market capitalization by approximately $80-100 billion.
Google’s AI Semiconductor Strategy
On October 23, Google, through its cloud services subsidiary, announced a multimillion-dollar deal to provide tensor processing units (TPUs) to AI company Anthropic. The agreement marks the first time that Google’s TPU technology has stood up to the growing demand for AI after 10 years of development. In addition, Google also announced a breakthrough in quantum computing algorithms, paving the way for future increases in computing power.
Manufacturing applications:
These advances in semiconductor technology mean a dramatic increase in AI processing power in manufacturing. Fast AI inference in edge computing environments will make real-time quality inspection, predictive maintenance, and process optimization a reality. In particular, dedicated chips such as TPUs will enable cost-effective AI implementations and facilitate access to AI technology for small and medium-sized manufacturing operations.
2. innovation in robotics and automation technology
Amazon’s “Blue Jay” robot is now available
On October 22, Amazon announced Blue Jay, a new AI robot for distribution facilities. The robot uses AI to autonomously sort and transport packages, allowing a single robot to complete tasks previously performed by three different robots. Currently in operation at its South Carolina location, the company plans to introduce thousands of these robots to other locations.
However, according to internal documents reported by the New York Times, Amazon could save more than 160,000 jobs in the U.S. in two years and more than 500,000 by 2033 by increasing efficiency through AI robots. The company’s goal is to automate 75% of its operations, which is a key indicator of its impact on employment in the manufacturing sector as a whole.
GrayMatter Robotics’ Innovation Center
On October 23, GrayMatter Robotics opened its new 100,000 square foot (9,300 square meter) headquarters and innovation center in Carson, California. The center is intended to expand AI robotics applications in the manufacturing industry and will serve as a research and development center for “physical AI” (AI that operates in the physical world).
Rapid Growth of Apera AI
Apera AI is ranked 10th in the Deloitte Technology Fast 50 for 2025 as a leader in 4D vision technology for industrial robot automation. The company’s AI-powered vision systems enable the recognition and understanding of objects with complex geometry and reflective properties for flexible automation in manufacturing.
Manufacturing applications:
These robotics technologies have the potential to significantly automate labor-intensive processes in the manufacturing industry. Of particular note is the shift from automation of simple tasks to those involving complex decisions by AI. Processes that previously required human judgment, such as quality inspections, component picking and placement, and assembly work, are also subject to automation. However, considering the impact on employment, worker retraining and skill transfer must be promoted in parallel.
3. evolution of generative AI technology
Official release of OpenAI’s Sora 2
OpenAI has officially released Sora 2, a text-to-video generator. The model features improved temporal consistency, a deeper understanding of the laws of physics, and the ability to generate up to 60 seconds of cinematic-quality video. the iOS-only, invitation-only app achieved 1 million downloads in 5 days. Key features include improved realism with natural motion and lighting, scene composition with multiple object interactions, and consistent representation of characters.
Google “Veo 3.1” announcement
Google has released Veo 3.1, a video generation AI model that offers improved image-to-video conversion quality, integrated audio capabilities, and more precise editing tools. It features consistent object insertion within scenes and reference-based animation, and is deployed across the Flow, Gemini, and Vertex platforms.
Extended search functionality for ChatGPT
OpenAI has released ChatGPT Search free of charge for all users. This feature provides fast and timely answers while citing relevant web sources, making it a direct competitor to Google’s search dominance. Optimized mobile versions and advanced voice search capabilities have also been introduced, allowing users to ask questions and get answers with voice commands.
Manufacturing applications:
Generative AI technology is revolutionizing documentation, training material development, and product prototype visualization in manufacturing, and video generation technologies like Sora 2 can be used to animate work procedures, create simulation videos for safety training, and demonstrate products to remote locations. product demonstrations in remote locations. In addition, ChatGPT Search greatly streamlines searching for technical documents, quickly obtaining troubleshooting information, and accessing regulatory and standards information.
4. manufacturing industry-specific AI implementation trends
Seven Levels of AI and Practical Application
According to an article by the Forbes Business Council, AI in manufacturing falls into the following seven stages
- Rule-based logic: predetermined decisions based on specific inputs
- Basic machine learning: recommendations based on historical datasets
- Pattern recognition: similarity identification of complex data sets using deep neural networks
- Large-scale language model (LLM): processing unstructured input and generating human-like responses
- Deterministic Optimization: Explicit problem model incorporating physical laws and performance indicators
- Sequential decision problem: Reinforcement learning considering the future impact of current decisions
- Advanced intelligence: problem solving using creativity, logic, reasoning, and emotion (theoretical stage at this time)
Currently, Levels 1 through 5 are the main levels of practical application in the manufacturing industry, with Level 5 deterministic optimization in particular having great potential for micro-level optimization of manufacturing processes.
Problem Solving through AI Integration
Key challenges facing the manufacturing industry include
- Widening Skills Gap: The projected shortage of 2 million manufacturing workers in the U.S. by 2033 as experienced engineers retire outpacing the influx of new talent
- Higher material costs: Inflation, supply chain tensions, and global competition have increased the cost of raw materials, tools, and supplies.
- Increasing product design complexity: Sophisticated products place increased demands on quality assurance and production flexibility
- Global Uncertainty: Rising Trade Friction Creates Challenges Across Supply Chains
For these challenges, AI is the key to practical automation solutions that save time, control costs, and facilitate production.
Manufacturing AI Trends in Japan
Smart Factory EXPO 2025
The Smart Factory EXPO, to be held at Port Messe Nagoya on October 29-31, 2025, will showcase the latest technologies to realize Smart Factory DX with IoT, AI, and FA. This exhibition will be an important indicator of how the Japanese manufacturing industry is adopting AI technology.
Accelerate the use of generative AI
According to the AWS report, as of October 2025, the use of generative AI in Japan will first focus on improving productivity, in other words, increasing the efficiency of work performed by humans. While production is becoming increasingly automated in the manufacturing industry, human resource shortages and technology transfer are major issues, and generative AI is expected to contribute to solving these problems.
Strengthening U.S.-Japan AI Cooperation
The Japanese government will formulate a basic plan to promote research and development and utilization of AI by the end of 2025. The plan envisions the development of domestically produced AI using high-quality data and its deployment in overseas markets such as the Global South. In addition, the two countries have agreed to strengthen cooperation in seven areas, including AI and advanced telecommunications, which will expand opportunities for Japanese manufacturers to access the global AI ecosystem.
6. specific application scenarios for the manufacturing industry
Advancement of predictive maintenance
Predictive maintenance, which predicts equipment failures in advance through a combination of AI and IoT sensors, is evolving dramatically. The shift from traditional scheduled maintenance to “condition-based maintenance” based on the actual condition of machinery is accelerating, resulting in reduced downtime and optimized maintenance costs. AI that solves Level 6 sequential decision-making problems can develop optimal maintenance plans that even take into account the impact of current maintenance decisions on future production schedules.
Automated quality inspection
With advances in pattern recognition technology (Level 3), the accuracy of defect detection by image recognition is increasingly outperforming human visual inspection. 4D vision technology from companies like GrayMatter Robotics and Apera AI can detect even the smallest defects in parts with complex geometry and surface characteristics. with complex geometry and surface characteristics. This allows the inspection process to be fully automated and operate 24 hours a day, greatly improving the consistency of product quality.
Supply Chain Optimization
Staffing optimization AI, such as that presented by Amazon, can also be applied to supply chain management in the manufacturing industry. By integrating warehouse and production line availability data, staffing, inventory levels, and transportation routes can be optimized to improve overall efficiency. In particular, the use of LLM for demand forecasting enables highly accurate demand forecasting that even takes market trends and social media trends into account.
Digital Twin and Simulation
The combination of deterministic optimization (Level 5) and digital twin technology enables virtual simulation of the entire manufacturing process. The ability to optimize in the digital space before actually introducing a new product, reconfiguring a production line, or installing new equipment reduces risk and maximizes return on investment. video generation technologies such as Google Veo 3.1 can help visualize the results of these simulations and present them in a way that is easily understood by non-technical personnel. help present them in a way that is easy for non-technical users to understand.
Worker Support and Training
Generative AI revolutionizes the training of new workers, enabling learning support in a variety of forms, including realistic work procedure videos created with OpenAI Sora 2, interactive training with ChatGPT, and real-time work guidance combined with AR glasses. This will accelerate the transfer of skills while addressing the issue of a shortage of skilled technicians.
7. implementation issues and points to note
Employment Impact and Social Responsibility
As Amazon’s internal documents show, AI automation can lead to massive job cuts. As manufacturers implement AI, it is imperative that they do more than simply increase efficiency; it is essential that they engage in socially responsible initiatives such as worker retraining, skill transformation programs, and the creation of new roles. The concept of “human-centered” AI implementation is an important guiding principle for building a future where technology and humans work together.
Data Quality and Security
AI performance depends on the quality of data. Ensuring the accuracy, completeness, and consistency of data collected from manufacturing sites is not easy. In addition, the manufacturing data collected and processed by AI systems contains information that can be a source of competitive advantage for companies, so ensuring cybersecurity and data privacy is critical.
Evaluation of return on investment
Investing in AI technology can be expensive, especially for smaller companies, and as the Forbes article points out, manufacturing leaders should focus on customer outcomes and not be distracted by “AI purity” or general technology hype. Even if the technology foundation is not strictly AI, a solution that looks, behaves, and delivers results intelligently has practical value.
Organizational Culture Change
AI implementation requires not only technological change, but also a transformation of organizational culture. Transformation of the entire organization is the key to success, including a shift to data-driven decision making, fostering a culture of experimentation that allows for failure, and strengthening cooperation between departments.
Conclusion.
As this week’s news shows, AI technology is accelerating as an unstoppable trend in the transformation of the manufacturing industry. Breakthroughs in semiconductor technology, the practical application of advanced robotics, and the proliferation of generative AI have the potential to transform every aspect of manufacturing.
However, successful technology adoption is not simply about adopting state-of-the-art AI, but about focusing on specific business outcomes, emphasizing human-technology collaboration, and implementing it in a socially responsible manner. The Japanese manufacturing industry has strengths in high quality standards, a culture of continuous improvement, and an emphasis on skilled trades. By appropriately combining these strengths with AI technology, Japan will be able to maintain and strengthen its advantage in global competition.
The challenge for the future is the “democratization” of AI technology. The year 2025 is also known as the “first year of AI agents,” and it is expected that business automation and productivity improvement will further accelerate. The Japanese manufacturing industry has a long history of achieving this goal. At this historic turning point, Japan’s manufacturing industry needs to build a sustainable, human-centered future while proactively embracing AI technology.
Source List
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- Asahi Shimbun Digital (October 25, 2025). “AI sorting robots seen in Amazon warehouse, will they take 500,000 jobs?” https://www.asahi.com/articles/ASTBS04WZTBSUHBI015M.html
- Forbes Business Council (October 22, 2025). “AI In Manufacturing: Moving Beyond The Hype To Real Business Value” https://www.forbes.com/councils/forbesbusinesscouncil/2025/10/22/ai-in- manufacturing-moving-beyond-the-hype-to-real-business-value/
- TST Technology (October 16, 2025). “Mid-October 2025 AI & Tech News: Key Global Updates” https://tsttechnology.io/blog/mid-october-ai-news-2025
- Reuters (October 23, 2025). “Anthropic to use Google’s AI chips worth tens of billions to train Claude chatbot” https://www.reuters.com/technology/anthropic-expand-use- google-clouds-tpu-chips-2025-10-23/
- PRNewswire (October 23, 2025). “GrayMatter Robotics Unveils 100,000-Square-Foot AI Robotics Innovation Center in Carson”. https://www.prnewswire.com/news-releases/ graymatter-robotics-unveils-100-000-square-foot-ai-robotics-innovation-center-in-carson-302592147.html
- Automate.org (October 23, 2025). “Apera AI Named Winner and Ranks 10th Fastest Growing Company in 2025 on the Deloitte Technology Fast 50 List. ” https://www.automate.org/news/apera-ai -named-winner-and-ranks-10th-fastest-growing-company-in-2025-on-the-deloitte-technology-fast-50-list
- Nihon Keizai Shimbun (October 24, 2025). “Japan, U.S. to cooperate in seven areas, including AI and advanced telecommunications.” https://www.nikkei.com/article/DGKKZO92165760U5A021C2MM8000/
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- Manufacturing Skills Institute (2024). “2024 Deloitte and the Manufacturing Institute Workforce Study.” https://manufacturingskillsinstitute.org/highlights-from-the-2024-deloitte -and-the-manufacturing-institute-workforce-study/
