Introduction.
The opening of the year 2026 has been a week of fundamental redefinition of “how to deal with AI” for the manufacturing industry. Google’s new models were unveiled to cut through the year-end silence, and warnings of an escalating semiconductor shortage. These are not mere technology news, but rather signals that AI has moved beyond “laboratory artifacts” to become a “real-world force” responsible for decision-making on factory lines and in supply chains. This paper unpacks the major news stories of this tumultuous week and examines the changes for which the manufacturing floor must prepare now.
1. Google Gemini 3 Flash: A New Era of High-Speed Reasoning
On December 17, 2025, Google officially released Gemini 3 Flash. This model was developed based on the concept of “frontier intelligence at speed” and outperforms the Gemini 2.5 Pro in many benchmarks, while delivering faster processing at a lower cost.
Manufacturing Applications
In the manufacturing industry, fast AI models like Gemini 3 Flash have the potential to revolutionize real-time quality inspection, predictive maintenance, supply chain optimization, and more. Of particular note is the inclusion of a dedicated inference layer called “Thinking Mode. This will enable problem solving and optimization decisions for complex manufacturing processes to be performed at high speed without human intervention.
For example, a system that instantly recognizes patterns and analyzes the causes of defects that occur on a manufacturing line and proposes automatic adjustments to manufacturing parameters can be realized. Areas that previously relied on the experience and intuition of skilled engineers can be standardized and accelerated by AI, resulting in a dramatic increase in productivity.
2. growing shortage of AI chips: impact on manufacturing industry
At the end of 2025, a surge in AI demand revealed a severe supply shortage of memory chips, especially High Bandwidth Memory (HBM); NPR reports that chipmakers will reach their production capacity limits by the end of 2026, and this shortage is expected to continue until late 2027 The shortage is expected to continue through the second half of 2027.
Impact on the manufacturing industry and how to respond
This semiconductor shortage will have a dual impact on the manufacturing sector. First, it will make it difficult to obtain the chips needed to implement smart factories and IoT devices, which will delay digitization. Second, the cost of implementing AI-based manufacturing optimization systems will rise.
However, this crisis is also an opportunity: according to Deloitte’s 2026 Manufacturing Outlook, advanced manufacturing companies are leveraging “agentic AI” (autonomous AI) to achieve highly efficient operations even with limited computing resources. Specifically, a strategy to minimize the impact of semiconductor shortages by leveraging edge computing and reducing reliance on the cloud will be effective.
3. agentic AI: the new frontier of manufacturing
One of the most notable trends in late 2025 and early 2026 is Agentic AI (Agentic AI). This refers to AI systems that achieve their goals autonomously without waiting for human instructions.
LTIMindtree Success Stories
LTIMindtree, an Indian IT company, announced that it has increased revenues by $60 million ($9.4 billion) in the first half of FY2025 through agentic AI. The company has deployed more than 1,500 “digital employees,” each assigned an employee ID and manager. This is a symbolic example of how AI has begun to function as a member of the organization, rather than just a tool.
Manufacturing Applications: Forecast to 2026
Gartner predicts that by 2026, 40% of enterprise applications will incorporate task-specific AI agents. In the manufacturing industry, the following applications are expected
Autonomous production management: AI agents analyze inventory status, equipment utilization rates, and demand forecasts in real time to dynamically optimize production schedules. Adjustments that used to take production managers several hours to perform can be completed in minutes.
Autonomous supply chain operations: when logistics delays occur, AI agents will automatically select alternative suppliers and execute the ordering process; Forbes predicts that by 2026, agentic AI will manage logistics and production end-to-end, reroute inventory in real time and accelerate shipments, and dynamically adjust manufacturing as needed.
Evolution of predictive maintenance: Continuously analyzes data from equipment sensors and automatically orders maintenance parts and adjusts maintenance schedules when signs of failure are detected.
4. China’s AI and Semiconductor Breakthroughs: Geopolitical Implications
On December 31, 2025, in his year-end speech, Chinese President Xi Jinping positioned 2025 as “the year of breakthroughs in AI and semiconductors,” emphasizing that despite U.S. technology restrictions, China has made significant progress in its own AI and semiconductor technologies.
Of particular interest are reports that Chinese companies such as DeepSeek have developed large-scale language models with sexual maturity comparable to OpenAI’s ChatGPT without using high-performance AI semiconductors from Nvidia, Inc.
Impact on manufacturing
This trend has important implications for the manufacturing industry. First, the diversification of AI technologies reduces the risk of dependence on specific technology platforms. Second, it opens up a wider choice of cost-effective AI solutions.
For manufacturing companies with global operations, it is imperative to develop an AI strategy that takes geopolitical risks into account. A “multi-vendor strategy” that selects the best AI solution for each region, such as Chinese AI for the Chinese market and Western AI for the U.S. and European markets, is a realistic option.
5. international coordination of AI governance: an important decision in January 2026
According to the ETC Journal analysis, January 2026 is the time when three key decisions will be made in international AI governance:
- Fragmentation or collaboration: Will each country promote its own AI regulations or collaborate in a global framework?
- Degree of centralization: How much control does the government have over the development and operation of frontier AI (state-of-the-art AI)?
- Zero-sum or collaborative development: treating AI as a geopolitical weapon or a common global development tool?
Impact on manufacturing
These decisions will directly impact manufacturing AI investment strategies. If there is greater global coordination, it will be easier to deploy a single AI system in multiple countries. On the other hand, if fragmentation increases, then different regional compliance requirements will need to be addressed and implementation costs will rise.
The World Economic Forum advocates the creation of a “global AI operating system,” which would facilitate interoperability for safety certification and risk assessment. Manufacturing companies should keep a close eye on these international trends and build an AI architecture that can respond flexibly.
6. scientific breakthroughs using AI
According to the Axios report, 2025 will be recorded as the year in which AI accelerated scientific research. In particular, significant progress was made in the following areas
- Medical Diagnostics: Diagnosis of Alzheimer’s Disease Becomes Faster and Less Expensive
- Drug Discovery: Google’s AlphaGenome to Support Disease Understanding and Drug Discovery
- Weather Forecasting: Combining AI and Physical Models to Predict Extreme Weather
- Materials Science: MIT Team Uses AI to Discover Alternative Cement Materials
Manufacturing Applications
These scientific breakthroughs can also be applied to the manufacturing industry. For example, the use of AI in materials science can significantly shorten the development time of new materials. Material development processes that previously took several years could be reduced to a few months through AI-based literature analysis and simulation.
The Fujitsu/Kirin collaboration is another good example of product development utilizing “digital twin” technology. When applied to manufacturing, this approach minimizes physical prototyping and optimizes product design on a simulation basis.
7. manufacturing AI forecast for 2026: transition to practice
McKinsey’s 2025 AI Survey and Deloitte’s 2026 Manufacturing Outlook, taken together, place 2026 as the “AI maturity period”. Key projections include:
Clarification of return on investment
AI adoption will become a mainstream business decision as the return on investment (ROI) of AI becomes measurable for many manufacturing companies, and according to Deloitte, continued investment in agency AI is key to competitiveness and agility.
The Rise of Multi-Agent Systems
Multi-agent systems, in which multiple AI agents cooperate to complete complex workflows, will be put to practical use. For example, procurement agents, production planning agents, and quality control agents work together to optimize the entire manufacturing process.
Evolution of human-AI collaboration
AI will take root as a tool to expand human capabilities rather than take away human jobs. AI mentor” systems, for example, in which AI learns the knowledge of skilled workers and uses it to train younger technicians, will be put to practical use.
Strategies for manufacturers to take
Based on the above trends, we recommend strategies that the manufacturing industry should take for 2026 and beyond:
1. gradual transition to agentic AI
Start with specific tasks (quality inspection, inventory control, etc.) and gradually expand the scope of application; it is important to design an organization like LTIMindtree, where the AI has clear roles and responsibilities.
Adoption of a multi-vendor strategy
Avoid dependence on specific AI platforms and build flexible architectures that combine multiple AI solutions. Address both geopolitical and technology obsolescence risks.
3. investment in edge computing
To address semiconductor shortages and latency issues, reduce dependence on the cloud and enhance data processing capabilities within the plant.
4. human resource development and AI literacy
Foster a culture where not only engineers, but all employees, including management, understand the basics of AI and can utilize it. By understanding “what AI can do,” innovation will emerge from the field.
5. involvement in international AI governance
Through industry associations, we will participate in discussions on AI regulations and contribute to the formation of rules that are in line with manufacturing industry practices.
8. acquisition of Manus by Meta: tectonic shifts in the AI agent market
On December 29, 2025, Meta Platforms announced the acquisition of Singapore-based AI agent developer Manus for more than $2 billion. The acquisition is one of the most significant AI-related news at the end of 2025, symbolizing the maturation of the AI agent market and the intensifying battle for practical AI technology by giant tech companies.
What is Manus?
Launched in the spring of 2025, Manus is a general-purpose AI agent that gained attention for its ability to autonomously perform complex tasks such as market research, coding, data analysis, job candidate screening, travel planning, and stock portfolio analysis. The company claims to outperform OpenAI’s Deep Research agent and has achieved more than $100 million in annual recurring revenue (ARR) in just eight months since launch.
Of note is the amount of data processed by Manus. According to the company, it has processed more than 147 trillion tokens of text and data and supported more than 80 million virtual computers. This scale indicates that AI agents are being used on a large scale in real business environments.
Strategic Significance of Acquisitions
For Meta, this acquisition is important in multiple ways:
Path to Monetization: Meta CEO Mark Zuckerberg has invested $60 billion in AI infrastructure, but monetization has been a challenge; Manus is an AI product that is actually generating revenue and provides a concrete model for monetizing the Meta AI assistant.
Talent Acquisition: The entire Manus team will join Meta to gain expertise in advanced AI agent development. This is an important win in the competition for talent with Google and OpenAI.
Platform Integration: Meta has announced plans to integrate its AI agent technology into Facebook, Instagram, and WhatsApp while operating Manus independently. This will give billions of users access to AI agents.
Geopolitical Aspects: Relations with China
The acquisition of Manus has a complex geopolitical component. The company was originally founded in China as Butterfly Effect (also known as Monica.Im) and moved its headquarters to Singapore in June 2025. Major investors include Chinese firms such as Tencent, ZhenFund, and HongShan Capital (formerly Sequoia China).
In this regard, U.S. Senator John Cornyn had expressed concern about the flow of U.S. capital to China-related companies. In response, Meta stated clearly that “after the acquisition, Manus will not have any continuing Chinese ownership, and its services and operations in China will cease.
However, this move highlights the complex relationship between international talent and capital mobility and geopolitical tensions in AI technology development. The composition of technologies developed by talented Chinese entrepreneurs being acquired by U.S. companies via Singapore may be one pattern for the global AI industry in the future.
Application to manufacturing: Manus shows potential
Manus technology has direct applicability to the manufacturing industry:
Automation of complex business processes: Manus is not a simple automation tool; it can perform tasks that involve complex decisions. For example, Manus can automate tasks that previously could only be performed by humans, such as creating supplier evaluation reports, recommending the best supplier from multiple candidates, and even presenting negotiation points for contract terms.
Research and Market Analysis: In new product development, AI agents can autonomously perform market research, competitive analysis, and technology trend identification, and generate hundreds of pages of detailed reports. This allows product planners to spend more time on strategic decisions.
Coding and Systems Development: While the digitization of manufacturing floors requires the development of custom software and dashboards, agents such as Manus can understand the requirements, generate code, and even perform testing. This reduces dependence on IT departments and accelerates shop-floor-driven digitization.
Data analysis and decision support: Production data, quality data, inventory data, and other data are generated in the manufacturing industry in vast quantities. AI agents are expected to be used to analyze these data in an integrated manner and propose optimal production schedules and inventory levels.
Partnering with Microsoft: Embedding into the Ecosystem
It is worth noting that prior to the acquisition, Microsoft had begun testing Manus on Windows 11 PCs in October 2025. Users are able to create websites from local files, which suggests a trend toward AI agents being integrated at the OS level.
For the manufacturing industry, this has important implications. The natural integration of AI agents into existing software ecosystems, such as factory management systems, ERP systems, and CAD software, will allow anyone to benefit from AI without special training.
Outlook for 2026: The AI Agent Wars Intensify
The acquisition of Manus heralds that 2026 will be the year of big M&A in the AI agent market, and Meta’s $2 billion windfall will increase the likelihood that other major tech companies (Google, Microsoft, Amazon, Apple) will accelerate their acquisitions of similar AI agent companies, increasing the likelihood that they will accelerate their acquisitions.
For manufacturing companies, this competition is a boon as the technology matures and prices drop. At the same time, however, it creates new strategic challenges: which platform to choose and how to integrate multiple platforms.
What is important is that “AI agents that actually generate revenue,” as Manus has shown, are real. This means that AI agents are no longer in the research phase, but have entered the practical phase. In the manufacturing industry, 2026 will be the year to seriously consider implementing AI agents.
Conclusion
The week of December 28, 2025-January 3, 2026 was a period of multiple historic events in the AI industry: the arrival of Google Gemini 3 Flash, the growing shortage of AI chips, the practical application of agentic AI, the advancement of AI technology in China, and Metaacquisition of Manus by Meta, all indicate that AI technology is moving from the “experimental” stage to the “practical/monetization” stage.
The Manus acquisition, in particular, proved that AI agents can be a viable business model. The rapid growth of the company, reaching an ARR of $100 million just 8 months after launch and being acquired for $2 billion, epitomizes the dynamism of the AI market.
For manufacturers, these are not just technological trends, but strategic issues that will affect their competitiveness. 2026 will mark the transition from the “try it out” phase to the “full-scale implementation” phase of AI. Leading companies have already started to redesign their business processes with agentic AI, and falling behind will directly lead to a loss of competitiveness in the market.
The onsite capabilities and kaizen culture of the Japanese manufacturing industry can become a new strength when combined with AI. The realization of “extended manufacturing” that combines human creativity and AI processing power will be a key theme in 2026 and beyond.
Source List
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