The Global AI Industry Is Entering a New Phase

Artificial intelligence is undergoing a major transformation.

Over the past few years, the global technology industry has focused primarily on chip performance, large-model capabilities, and breakthroughs in generative AI. The key questions were: Who has the most powerful chips? Whose large models are the most intelligent? Who can be the first to launch the next generation of AI products?

However, as artificial intelligence gradually expands into corporate operations, industrial manufacturing, financial services, and public services, a more practical question is emerging:

As more companies begin to adopt AI, what kind of infrastructure will be needed to ensure that artificial intelligence can operate effectively and continue creating value?

In October 2026, several major technology events around the world provided important signals.

From October 12 to 15, the OCP Global Summit 2026 was held in San Jose, United States. The event highlighted innovations in networking, cooling, and system architecture designed for large-scale AI computing.

On October 21, NVIDIA presented a modular system design for AI data center development at its GTC event in Berlin, Germany, exploring new ways to shorten infrastructure deployment cycles.

Meanwhile, the Global AI and Digital Summit, held in Seoul, South Korea, from October 19 to 22, expanded the discussion to how AI can improve productivity, enhance public services, and support practical development across different industries.

From data center technologies and AI system development to real-world applications, these international developments reflect a common trend: competition in the AI industry is expanding beyond individual technological capabilities into a broader contest involving infrastructure, system integration, and industrial applications.

Global Focus One: NVIDIA Introduces a New Modular AI Factory Solution

On October 21, NVIDIA unveiled the DSX POD reference design at its GTC event in Berlin, Germany. Designed for AI factories, the modular system is integrated and validated by partners in a factory before being transported to a deployment site with the required supporting conditions.

According to information released by NVIDIA, when site power, cooling, networking, and other supporting facilities are ready, the solution could reduce system deployment time from approximately six months to as little as 14 days. NVIDIA stated that its partners plan to begin offering modular AI factory systems to global markets in early 2027.

The significance of this announcement extends beyond the introduction of a new hardware product.

It reflects a broader shift in the way AI infrastructure is being developed. The industry is placing greater emphasis on standardized design, pre-integration, system validation, and rapid deployment, with the goal of bringing computing capacity into operation more quickly.

Why is deployment speed so important?

An AI data center can be compared to a large central kitchen.

Purchasing advanced kitchen equipment does not mean that the kitchen can begin operating the next day. The site must be prepared, water and electricity must be connected, equipment must be installed, operating procedures must be tested, and every part of the system must work together.

The same is true for AI data centers.

Even with access to advanced computing chips, computing resources cannot perform effectively if the servers, networks, power supply, and cooling systems are not ready. After the equipment has been deployed, system integration, software configuration, operational testing, and business adaptation must still be completed.

Shortening the construction and deployment cycle therefore represents more than an improvement in engineering efficiency. It may also allow companies to gain access to usable computing capacity sooner.

However, modular design does not mean that every data center can be completed within 14 days. NVIDIA’s stated timeframe depends on the specified system design and the conditions at the deployment site. It should not be interpreted as the complete timeframe required for site selection, regulatory approval, construction, and the commencement of full data center operations.

This development demonstrates that the future value of AI infrastructure will increasingly depend on the ability to integrate chips, servers, networking, energy, cooling, and software into a stable and fully operational system.

Global Focus Two: As AI Computing Expands, Data Center Networking and Cooling Become Equally Critical

While NVIDIA is focused on accelerating the deployment of AI systems, the OCP Global Summit held from October 12 to 15 highlighted another important direction: as the scale of AI computing continues to grow, the efficiency of collaboration within data centers is also becoming critical.

At the summit, Meta introduced several networking technologies designed for large-scale AI workloads, including liquid-cooled network racks, a 102.4 Tbps switching platform, and network architectures capable of interconnecting larger GPU clusters.

Although these technologies may appear distant from everyday life, they are closely related to whether AI services can operate efficiently.

Imagine a company deploying a large number of computing devices to train models, process customer requests, and provide AI services to different business departments simultaneously.

These devices do not operate independently. They must continuously exchange data, share resources, and work together to complete computing tasks according to specific requirements.

If data cannot move smoothly between devices, overall operating efficiency may still be limited, even when each individual device offers strong performance.

At the same time, cooling is becoming an increasingly important issue as computing density rises. The heat generated by computing equipment must be removed promptly; otherwise, the system may be unable to maintain its expected level of performance.

This can be compared to an expanding production line. Even if the machinery becomes more advanced, the overall system cannot operate at peak efficiency if transportation routes are too narrow, the power supply is unstable, or temperature controls are inadequate.

AI infrastructure development is therefore moving beyond the pursuit of computing power alone and toward the integrated optimization of computing, networking, storage, cooling, and system coordination.

This also explains why global technology companies are investing not only in chip research and development, but also in data center architecture, network interconnectivity, energy efficiency, and system management.

Competition in the future AI infrastructure market will not simply be determined by who owns the greatest number of computing devices. It will also depend on whether those devices can work together efficiently and whether the overall system can operate reliably and economically.

Global Focus Three: The Ultimate Goal of AI Development Is to Bring Technology into Real-World Industries

From October 19 to 22, the World Bank Group and the Government of South Korea jointly organized the Global AI and Digital Summit.

Unlike technology events focused primarily on chip performance or model capabilities, the summit placed greater emphasis on how artificial intelligence can promote economic development, improve productivity, and create practical value for different regions and industries.

The summit covered agriculture, healthcare, education, tourism, and government services. It also addressed access to digital infrastructure, the cost of AI adoption, industrial ecosystems, and the practical conditions required to implement related technologies.

These topics highlight an important issue facing the industrialization of artificial intelligence:

Not every company needs to develop the most advanced large model independently, but an increasing number of companies need to identify the AI capabilities best suited to their own operations.

For example, a manufacturing company may want to use AI to identify product defects and reduce the workload associated with manual inspections. A retail company may want to analyze inventory and sales data with AI. A financial institution may need to deploy an internal knowledge-retrieval and risk-analysis system while satisfying strict data security requirements.

These needs are not identical.

Some companies require large-scale computing resources to train models. Others need only reliable inference services. Some place greater importance on keeping their data within internal systems and therefore require privately deployed solutions.

AI applications cannot remain limited to model demonstrations or basic question-and-answer functions. They must take into account computing resources, data conditions, software systems, business processes, security requirements, and ongoing operations and maintenance.

For industrial customers, what matters is not how many new technologies they use, but whether those technologies can help solve practical business problems.

This also means that the development of the AI industry requires the participation of multiple stakeholders. Infrastructure companies provide computing capacity; model and software companies develop technical solutions; industry enterprises contribute real-world requirements; and technical service teams manage deployment, adaptation, and continuous optimization.

Only when these different elements are effectively connected can artificial intelligence be transformed from a technological capability into productive industrial capacity.

Understanding the AI Infrastructure Value Chain Through Global Trends: What Matters Is a Complete System

Recent developments across the global technology industry demonstrate that AI infrastructure is not simply a collection of servers, chips, or data centers.

It is an integrated system composed of multiple interconnected components.

Foundational Hardware: Providing Computing Capacity

Chips, AI servers, memory, storage, and related hardware provide the computing foundation required for AI model training and inference.

Without sufficient computing resources, complex models cannot be trained efficiently, and large-scale AI services cannot operate reliably.

Data Centers and Supporting Facilities: Ensuring Continuous Operation

Power supply, cooling, network connectivity, data center environments, and security safeguards collectively determine whether computing resources can operate continuously.

As the scale of computing expands, the importance of these supporting facilities continues to grow.

Software Platforms and Computing Resource Orchestration: Improving Resource Efficiency

Different computing devices vary in performance, cost, and suitability for particular applications.

Through unified management, resource allocation, and task scheduling, computing tasks can be assigned according to actual requirements, helping companies use their available resources more efficiently.

AI Models and Applications: Turning Computing Capacity into Practical Value

Computing resources do not automatically create commercial value.

Only when models are integrated with business scenarios and used to solve practical problems can computing capacity be transformed into measurable business results.

Technical Services and Continuous Operations: Supporting Long-Term Use

After an AI system has been deployed, it still requires maintenance, performance optimization, model updates, security management, and adaptation to changing business needs.

For companies, the ability of a technology system to operate reliably over the long term is just as important as the performance of the hardware used during its initial construction.

The AI infrastructure value chain is therefore not a simple combination of several independent components. It is an interdependent system.

Chips determine part of the available computing capacity. Data centers provide the operating environment. Networking and orchestration affect resource efficiency. Models and applications connect computing resources with business requirements, while service capabilities help keep the overall system operating effectively.

Valuable AI infrastructure must address construction, integration, application, and operations as a complete process rather than focusing on a single component.

Beta Creative Technology: Advancing Industrial Collaboration Through AI Infrastructure, AI Applications, and AI Services

In response to changes in the global AI industry, Beta Creative Technology is pursuing business areas directly connected to the evolution of the AI value chain. The company is developing capabilities across three areas—AI Infrastructure, AI Applications, and AI Services—covering the journey from foundational computing capacity to enterprise-level applications.

AI Infrastructure: Providing the Foundation for AI Applications

Beta Creative Technology’s AI Infrastructure business includes independently developed AI chips, computing servers, edge nodes, and related technologies.

This infrastructure is designed to meet different computing requirements and provide a foundation for data centers, enterprise AI systems, and applications that require data to be processed close to the point at which it is generated.

As AI applications expand from the cloud into factories, industrial parks, retail stores, and other real-world environments, enterprise demand for computing resources will become increasingly diverse.

The appropriate hardware configuration, deployment model, and balance between performance, cost, and reliability will vary between different business scenarios. These are all important considerations in AI infrastructure development.

AI Applications: Using Foundational Computing Capacity to Meet Real-World Needs

The purpose of developing computing capacity is not simply to possess computing resources, but to ensure that those resources can be put to practical use.

Beta’s related business areas include AI chip supply, cloud computing capacity leasing, and privately deployed computing infrastructure. These services are intended to provide suitable computing capabilities and deployment solutions for different types of enterprise requirements.

For companies that are not currently suited to building their own data centers, cloud computing capacity can provide an alternative way to access resources. Companies with specific data security, internal system, or operating environment requirements may instead need to evaluate privately deployed solutions based on their individual business conditions.

The appropriate approach must be determined according to customer requirements, technical conditions, costs, and security considerations. A single solution will not be suitable for every company.

AI Services: Moving from System Deployment to Long-Term Operations

Launching an AI application does not mean that the work is complete.

Models require continuous optimization, systems require maintenance, computing resources must be orchestrated efficiently, and applications must be adjusted as business requirements evolve.

Beta has identified computing resource sharing, customized large models, and industry applications as key areas within its AI Services business. This direction focuses on maintaining a continuous connection between infrastructure and real-world business requirements, ensuring that computing capacity is not only deployed but also used effectively throughout subsequent operations.

Looking Ahead: The Value of AI Infrastructure Lies in Making Intelligence Work

From NVIDIA’s exploration of modular AI factory development and Meta’s advancement of large-scale AI networking architectures to international summits focused on the practical adoption of artificial intelligence, the global AI industry is demonstrating an increasingly clear direction.

Beta believes that future AI competition will no longer be defined solely by model parameters and algorithms detached from real-world infrastructure. Instead, it will be driven by the fundamental restructuring of an ecosystem built around computing capacity, green energy, and network architecture.

True intelligence must take root in a digital environment that is more flexible, cost-effective, and sustainable. Only by accelerating the development of next-generation AI infrastructure that integrates hardware and software can general artificial intelligence—AGI—translate technological progress into widely accessible productivity gains across industries.