As if to symbolize the busyness of the New Year, the AI industry this week saw a fierce battle for leadership between mega-tech companies, a showdown between Google and OpenAI, and the Japanese government’s first “AI Basic Plan” – a plan to develop a new AI system.
A series of developments observed this week suggest that AI technology has moved beyond the mere phase of competitive development and is ready to enter the “real business” of manufacturing. In this article, we will look back on a turbulent week and unpack the impact of these news on the manufacturing workplace and business decisions.
1. OpenAI vs. Google: Intensifying AI Technology Competition
Urgent release of GPT-5.2 and “Code Red
On December 11, OpenAI suddenly announced the release of GPT-5.2. Behind this release was reportedly a “code red” (emergency) issued internally by the company’s CEO Sam Altman. When competitor Google’s Gemini 3 outperformed OpenAI in several benchmarks, the company took thorough countermeasures, temporarily freezing non-core projects and focusing all resources on development.
GPT-5.2 has made great strides in general intelligence as well as in coding ability and understanding of long contexts. In particular, its ability to generate spreadsheets, build presentation materials, and process complex process-intensive projects has improved, and its economic value in business practice has increased significantly.
Google Gemini 3 Flash Offensive
Google, on the other hand, did not hold back, releasing the Gemini 3 Flash on December 17, making it the default model for Gemini applications. While far outperforming the previous generation Gemini 2.5 Flash, the Gemini 3 Flash is still competitive in some benchmarks with the higher-end Gemini 3 Pro and OpenAI’s GPT-5.2.
Notably, it scored 33.7% on the difficult benchmark Humanity’s Last Exam (37.5% on Gemini 3 Pro and 34.5% on GPT-5.2) and outperformed all competitors with 81.2% on MMMU-Pro, which measures multimodal inference. Furthermore, the disruptive pricing of $0.50 per million input tokens and $3.00 per million output will be a powerful incentive for companies to implement AI on a large scale.
Implications for Manufacturing: Revolution in Cost Performance
This simultaneous progression of “performance improvement and price reduction” is extremely important for the manufacturing industry. The following applications, which were previously overlooked due to cost, are now realistic options.
- Automatic generation and translation of technical documentation: Dramatically reduce the cost of creating huge volumes of multilingual manuals and specifications.
- Optimization of complex production planning: Automation of planning that takes into account variables across multiple processes and locations.
- Automatic generation of quality reports: Instant generation of reports for customer submission from inspection data.
2. the rise and reality of agent AI
2025 is the first year of “Agent AI
The year 2025 was arguably the year that “agent AI” became a citizen. Unlike traditional interactive AI, these systems can autonomously determine and execute multiple tasks given a goal, and keep moving until completion.
At the roundtable “The Power of Agentic AI” held in Las Vegas on December 2, discussions heated up as a trump card for business transformation. In addition, the establishment of the “Agentic AI Foundation” by the Linux Foundation and the planned release of the open-source framework “goose” are rapidly developing the ecosystem.
Applicability and challenges for manufacturing sites
In the manufacturing industry, Agent AI is expected to play a role like an “autonomous field supervisor.
- Autonomous production adjustment: Reorganizes production plans without human intervention in response to sudden demand fluctuations or equipment problems.
- Supply chain optimization: Autonomously coordinates delivery dates with multiple suppliers and places orders when parts are in short supply.
- Automated quality control: One-stop service from abnormality detection to cause analysis and corrective action implementation.
On the other hand, as the Bloomberg report points out, there is no denying the “hype ahead” aspect at this point. A careful verification process to ensure safety on site is essential for practical application.
3. the Japanese government’s AI master plan
From “lagging behind to a reversal of the offensive” (Japanese only)
On December 19, the government’s AI Strategy Headquarters (headed by Prime Minister Sanae Takaichi) formulated its first AI Basic Plan. This is a national strategy based on the AI Law enacted in May.
In the plan, Japan faces the harsh reality that the amount of private investment in AI is less than 1/100th of that in the U.S. (ranked 14th in the world in 2024). The plan clearly states that Japan will launch a “reverse offensive” centered on the development of highly reliable AI, while expressing a sense of crisis that “AI is influencing national power and is becoming a form of all-out warfare.
Expectations and Support for Manufacturing
It should be noted that while the introduction of the system in central and local government ministries and agencies has been a catalyst, we are also aiming to spread the system to industry.
- AI introduction support for SMEs: Strengthening support system in terms of both technology and finance.
- Development of AI unique to Japan: Development of specialized AI models that are strong in Japanese language by learning on-site knowledge (tacit knowledge) of the manufacturing industry.
- Ensure safety: Doubling the number of personnel in the AI safety organization to create an environment in which AI can be used with peace of mind.
Trends in the AI semiconductor market
Record growth and geopolitical risks
As of December 19, 2025, global semiconductor sales reached a record $697 billion. Driving this figure is undoubtedly the special demand for AI.
Meanwhile, geopolitical tensions are also on the rise. China is promoting the development of its own AI chips as a “Manhattan Project”-grade national project in response to regulations in Western countries. The U.S. is also tightening its screening of Nvidia’s latest chip (H200) exports to China, and the U.S.-China conflict is casting a shadow over the manufacturing supply chain.
Impact on manufacturing
Trends in semiconductors are directly related to the smartening of factories.
- Widespread use of edge AI: Increased availability of high-performance chips will enable real-time high-speed processing in the factory without the need for cloud computing.
- Cost reduction: Price competition among chips lowers the hurdle for capital investment.
- Diversification of supply sources: To hedge risk, there is an urgent need to diversify procurement routes.
Latest Trends in AI Application in the Manufacturing Industry
Predictive Maintenance: Moving to Maturity
In 2025, the phase of predictive maintenance with AI has shifted from “experimentation” to “standard equipment”; real-time anomaly detection is becoming a commonplace technology, as exemplified by the edge AI system from the partnership between Siemens and Arm.
- Benefits: 30-50% reduction in unplanned downtime and 20-25% reduction in maintenance costs.
Quality Control: Beyond the Human Eye
Image recognition AI is replacing and extending the “eye” of the skilled worker. Examples from Toyota’s magnetic inspection and Bridgestone’s tire molding process prove that AI can minimize quality variation and improve defect detection rates by more than 90%.
Production Optimization: Business Support by Generative AI
As seen in the company-wide introduction of Panasonic Connect, generative AI is accelerating the speed of indirect operations and improvement activities. AI is beginning to take root as an “excellent assistant,” searching for past trouble cases, translating multilingual manuals, and generating improvement proposals based on data.
6. challenges and countermeasures for the introduction of AI by the manufacturing industry
The $16 Trillion Challenge.”
Fast Company notes that “AI should theoretically be transforming manufacturing, but it is not yet fully functional on the shop floor. This is a challenge that represents a $16 trillion opportunity cost. The biggest barrier is the difficulty in connecting (integrating) legacy systems that have been running for years with the latest AI. There is also a serious lack of data fragmentation and skilled human resources.
Five elements common to successful companies
According to Deloitte’s research, companies with successful AI implementations have one thing in common.
- Clear AI strategy: The goal should be clear on what AI is to be used for.
- Phased implementation: Do not suddenly roll out company-wide, but start small and gain success experience.
- Data infrastructure development: integrating siloed data and putting it in a form that is AI readable.
- Human Resource Development: Be willing to invest in reskilling employees.
- Organizing agents: managing AI agents as a “workforce” rather than a “tool”.
7. outlook for 2026
The Evolution of Manufacturing AI and Japan’s Opportunity to Win
The World Economic Forum states that three barriers must be overcome to realize the true value of agent AI: infrastructure, reliability, and data.
However, this is where the Japanese manufacturing industry has a chance to win. High-quality onsite data accumulated over many years, onsite capabilities cultivated through kaizen activities, and the government’s “reliable AI” strategy. When these factors are combined, Japan may be able to build a unique position in the global AI competition.
Conclusion
The third week of December 2025 coincided with a turning point in the use of AI in the manufacturing industry, as lower prices for high-performance models and the formulation of national strategies coincided to steer the industry from “consideration” to “implementation”.
While results in predictive maintenance and quality control have certainly begun to emerge, there are still looming barriers to overcome, such as integration with legacy systems. The key to success is not the technology itself, but rather a clear strategy and investment in the “people” who can use it.
How will Japan’s manufacturing industry evolve in 2026, when agent AI and edge computing permeate the shop floor? The success or failure of the “turnaround offensive” will depend on the decisions we make today.
Source List
- Reuters – “OpenAI launches GPT-5.2 after ‘code red’ push to counter Google’s Gemini 3” (December 11, 2025) https://www.reuters.com/technology/openai- launches-gpt-52-ai-model-with-improved-capabilities-2025-12-11/
- TechCrunch – “Google launches Gemini 3 Flash, makes it the default model in the Gemini app” (December 17, 2025) https://techcrunch.com/2025/12/17/google- launches-gemini-3-flash-makes-it-the-default-model-in-the-gemini-app/
- Asahi Shimbun – “First government basic plan to use AI ‘from a late start to a reversal offensive,’ also strengthens safety mechanisms” (December 19, 2025) https://www.asahi.com/articles/ASTDL4R8JTDLUTFL00QM.html
- Fortune – “2025 was the year of agentic AI. How did we do?” (December 15, 2025) https://fortune.com/2025/12/15/agentic-artificial-intelligence- automation-capital-one/
- World Economic Forum – “3 obstacles to agentic AI adoption and how to overcome them” (December 18, 2025) https://www.weforum.org/stories/2025/12/3- obstacles-to-ai-adoption-and-innovation-and-how-to-overcome-them/
- Bloomberg – “Agentic AI in 2025 Brought More Hype Than Productivity” (December 18, 2025) https://www.bloomberg.com/news/newsletters/2025-12-18/ agentic-ai-in-2025-brought-more-hype-than-productivity
- Reuters – “How China built its ‘Manhattan Project’ to rival the West in AI chips” (December 17, 2025) https://www.reuters.com/world/china/how-china- built-its-manhattan-project-rival-west-ai-chips-2025-12-17/
- McKinsey – “The state of AI in 2025: Agents, innovation, and transformation” (November 5, 2025) https://www.mckinsey.com/capabilities/quantumblack/ our-insights/the-state-of-ai
- Deloitte – “The agentic reality check: preparing for a silicon-based workforce” (2025) https://www.deloitte.com/us/en/insights/topics/ technology-management/tech-trends/2026/agentic-ai-strategy.html
- Fast Company – “AI’s $16 trillion problem: It still isn’t working on the factory floor” (December 16, 2025) https://www.fastcompany.com/91446538/ai- manufacturing-factories
- World Economic Forum – “Protecting jobs and boosting productivity: How AI can transform manufacturing” (2025) (*The end of the source list was cut off, so this has been completed and corrected.)
