New Shift in the AI Industry: The Market Is Focusing on How Computing Power Can Be Converted into Real Business Value

The artificial intelligence industry is entering a new stage of development.

In the past, the global technology industry focused primarily on chip performance, large-model capabilities, and technological breakthroughs. As AI gradually expands into real-world scenarios such as enterprise operations, software development, intelligent customer service, and data analytics, the industry’s attention is shifting toward a broader set of questions: Do companies genuinely need more computing resources? Can cloud services sustain their growth? Can data center construction keep pace with demand? Can infrastructure investment be converted into actual business revenue?

On October 28, 2026, Microsoft, SK hynix, and data center infrastructure company Vertiv each announced their latest quarterly results, providing three different perspectives on the development of the global AI industry.

Microsoft’s performance reflects changes in demand for cloud computing services. SK hynix’s results demonstrate the demand created by AI servers for high-performance memory and storage products. Vertiv’s performance reflects changes in the market for data center power, cooling, and other supporting infrastructure.

Although these companies operate in different parts of the value chain, they collectively point to an important trend:

The development of the AI industry is extending computing demand beyond chips and servers to cloud services, storage, power supply, cooling, and complete data center systems.

Microsoft Azure Grows 48%: Cloud Computing Is Becoming a Key Platform for AI Services

On October 28, Microsoft announced its financial results for the first quarter of fiscal year 2027, which ended on September 30, 2026.

According to Microsoft’s official financial report, the company generated total quarterly revenue of US$92.3 billion, representing year-over-year growth of 19%. Microsoft Cloud revenue reached US$63.9 billion, an increase of 30%, while Azure revenue grew 48% year over year.

These figures show that Microsoft’s cloud business maintained strong growth momentum, with Azure continuing to serve as an important growth driver for the company.

It should be noted that Azure’s growth includes multiple cloud services and does not mean that Microsoft’s AI business alone grew by 48%. Nevertheless, when considered alongside Microsoft’s continued expansion of AI cloud services and enterprise intelligence applications, the results provide an important reference point for assessing demand for cloud computing and AI infrastructure.

Why is cloud computing essential to the development of AI?

Cloud computing can be understood as a computing service center that companies can access whenever they need it.

In the past, a company requiring substantial computing resources might have needed to purchase its own servers, build a data center, configure networks, and employ specialist personnel to maintain the systems. This required significant initial investment and meant that the company had to bear the cost of equipment upgrades, ongoing maintenance, and idle resources.

Through cloud computing, companies can use computing, storage, and software services according to their actual requirements without having to build all the necessary infrastructure internally.

As the adoption of AI applications increases, the types of tasks handled by enterprises are also changing. Companies may need to operate intelligent customer service systems, analyze large volumes of business information, deploy internal knowledge assistants, or use AI to support software development. These applications may all create sustained demand for computing resources.

Cloud computing is therefore no longer simply a platform for storing data and operating websites. It is also becoming an important way for companies to access AI computing capabilities.

For the industry, the key issue is not only whether cloud service revenue is growing, but also which customers, products, and real-world applications are driving that growth, and whether service revenue can cover the associated costs of computing resources, energy, and operations.

SK hynix Quarterly Revenue Exceeds KRW 100 Trillion for the First Time: AI Servers Drive Demand for High-Performance Storage

While Microsoft’s financial results reflect demand on the cloud services side, the latest results from SK hynix highlight another critical resource behind AI infrastructure: storage.

On October 28, South Korean memory chip company SK hynix announced its financial results for the third quarter of 2026.

The company reported quarterly revenue of KRW 104.6155 trillion and operating profit of KRW 83.3338 trillion, marking the first time its quarterly revenue had exceeded KRW 100 trillion. The company attributed this performance to growing demand for AI storage and increased sales of high-value-added products.

In terms of products, SK hynix reported growth in server DDR5 memory sales, accelerating shipments of HBM4, and continued expansion in high-performance solid-state drives and other products designed for data centers.

HBM, or high-bandwidth memory, is an important storage technology for high-performance computing. It provides GPUs and other computing processors with higher-bandwidth access to data, helping them handle complex AI computing tasks.

Why does AI computing require not only GPUs but also high-performance memory and storage?

A GPU can be compared to a highly capable team responsible for handling complex tasks, while memory functions like the team’s workspace.

Even if the team is highly capable, its efficiency will still be limited if the required data cannot be delivered to the workspace in time.

During the training and inference of large AI models, data must be continuously read, processed, and exchanged. The more powerful the computing chips become, the greater the system’s potential requirements for data transmission speed, memory capacity, and storage efficiency.

This means that simply increasing the number of GPUs will not resolve every AI infrastructure challenge. The ability of computing, storage, and networking resources to work together effectively will also influence overall efficiency.

SK hynix also stated that long-term agreements have secured more than half of its production capacity for 2027 and 2028. The company expects tight storage supply conditions to potentially continue until 2030. This represents the company’s assessment of the future market and does not necessarily mean that an equivalent level of shortage will occur.

For the AI value chain, this development indicates that high-performance memory and server storage are becoming essential components of infrastructure development. Supply capacity, product innovation, and cost control will also influence the industry’s overall development.

Vertiv Quarterly Sales Increase 61%: The Importance of Supporting Data Center Infrastructure Becomes More Evident

As more computing equipment enters data centers, another issue is becoming increasingly important: how can stable power, appropriate operating temperatures, and reliable infrastructure support be provided for all this equipment?

On October 28, Vertiv, a supplier of critical data center infrastructure, announced its financial results for the third quarter of 2026.

According to the company’s official announcement, quarterly net sales reached US$4.313 billion, representing year-over-year growth of 61%. Organic orders increased by more than 100%, and the company also raised its full-year guidance.

Vertiv’s business includes critical infrastructure for data center power and thermal management. Although these facilities do not directly train AI models, they are essential to the normal operation of servers and computing systems.

It is important to note that Vertiv serves the broader data center and digital infrastructure market. Its entire revenue cannot simply be classified as AI-related revenue. Nevertheless, the company’s results provide an important reference point for assessing demand for data center infrastructure.

Why do power supply and cooling affect the development of the AI industry?

As the computing density of AI servers increases, data centers must meet higher requirements for power supply, cooling, and operational management.

Traditional data center designs may not be suitable for every high-density AI computing environment. When developing new systems, companies must consider power capacity, power distribution architecture, cooling methods, equipment layout, and the ability to maintain continuous operations.

This can be compared to a factory adding a large amount of high-powered production equipment. If the electrical system is not upgraded at the same time, the equipment cannot begin operating as planned, even if it has already been installed. If cooling capacity is insufficient, the production line will also struggle to operate reliably over the long term.

AI infrastructure development therefore extends beyond purchasing chips and servers. Power, cooling, networking, and supporting data center facilities must also be planned in parallel.

From an industry perspective, AI development is creating new demand for infrastructure companies while also increasing the complexity of project construction and operations. Companies must balance deployment speed, energy efficiency, reliability, and long-term costs.

Behind Three International Developments: AI Infrastructure Is Forming a More Complete Industrial System

Examining the latest developments at these three companies together provides a clearer view of the structure of the AI infrastructure industry.

Microsoft reflects demand for cloud services and computing resources. SK hynix demonstrates demand for high-performance memory and storage products. Vertiv highlights the importance of supporting facilities such as data center power and cooling.

These three areas are interconnected, but they should not be treated as equivalent.

These figures demonstrate that growth in the AI industry is not limited to model development or chip manufacturing. It may spread throughout the entire value chain.

Increasing computing demand drives the deployment of server and storage resources. As server capacity expands, data centers face higher requirements for power, cooling, and networking. Once this infrastructure is operational, cloud service providers and other technology service companies can provide computing and AI services to customers.

However, the growth rates, profit levels, and operating risks of each part of the value chain are not the same. Strong demand in one segment does not mean that every company across the value chain will receive the same benefits.

Future analysis should focus not only on the scale of infrastructure investment, but also on actual customer demand, project delivery capabilities, resource utilization, service revenue, and operating costs.

From Industry Growth to Commercial Value: Three Issues Requiring Continued Attention

Are Computing Resources Actually Being Used?

Building servers and data centers is only the beginning.

If computing resources cannot be brought into service promptly, or if customer demand is insufficient, deployed equipment may still generate idle capacity and related costs.

Industry analysis must therefore distinguish between planned construction, completed deployment, resources that are actually available, and systems in continuous operation. It must also examine real customers and actual resource usage.

Can AI Services Generate Sustainable Operating Revenue?

AI infrastructure involves ongoing expenses for energy, equipment maintenance, technical personnel, networking, and depreciation.

Assessing whether a business can operate sustainably requires a clearer understanding of how customers pay, how contract terms are structured, whether services are delivered as agreed, and whether the resulting revenue can cover the associated costs.

Growth in industry demand is an important external condition, but it cannot replace verification of an individual company’s actual operating performance.

Can Infrastructure Adapt to Rapid Technological Development?

AI hardware and models continue to evolve. When deploying systems, companies must consider equipment compatibility, scalability, resource orchestration efficiency, and the cost of future upgrades.

If an infrastructure design lacks flexibility, it may meet short-term requirements but later face costly modifications and declining resource utilization.

Competition in future AI infrastructure will therefore involve more than equipment performance. It will also involve system integration, operating efficiency, and long-term service capabilities.

Business Opportunities Identified by Beta Through Changes in the Global AI Industry

The recent results announced by Microsoft, SK hynix, and Vertiv reflect developments in cloud computing services, high-performance memory and storage, and data center infrastructure, respectively.

Although these companies operate in different parts of the value chain, they collectively demonstrate that the continuing development of AI applications requires more than increasingly powerful computing chips. The market also needs complete infrastructure capable of supporting continuous AI operations, as well as service capabilities that connect computing resources with real business requirements.

For Beta Creative Technology, which is focused on the development of AI infrastructure, these changes provide an important perspective on the industry.

The AI value chain is becoming increasingly interconnected, extending from computing resources to real-world applications and continuous operations.

In the area of AI Infrastructure, the industry must consider more than the performance of computing equipment. Resource allocation, system integration, operating efficiency, and the long-term stability of infrastructure are also essential. As AI computing requirements continue to evolve, organizing and using computing resources for different scenarios will remain an important area of focus.

In the area of AI Applications, companies are primarily concerned with whether AI can solve real business problems. Whether the use case involves model training, intelligent inference, or the intelligent transformation of internal enterprise systems, computing resources must be aligned with specific business requirements. For service providers, understanding what customers need, how systems should be deployed, and how usage costs can be controlled is just as important as providing computing capacity.

After an AI system has been deployed, it still requires technical maintenance, performance optimization, resource management, and continuous support. The reliability of the underlying infrastructure and the application’s ability to adapt to changing business requirements will also affect the customer’s long-term experience.

This is the industrial context behind Beta’s focus on AI Infrastructure, AI Applications, and AI Services. One end connects to computing infrastructure, while the other connects to real business requirements. Between them, technical integration and continuous operational capabilities are also required.

Industry demand growth does not mean that every related company will achieve the same results. For Beta, future business development must still be based on specific customer requirements, collaborative projects, resource integration, technical delivery, and actual operating performance.

From this perspective, observing the global AI industry involves more than identifying which companies are growing faster or which technologies are attracting attention. More importantly, it requires an understanding of how different parts of the value chain work together and how computing resources can be converted into sustainable service capabilities.

Industry trends provide direction, real business determines value, and continuous operations test capabilities. This is also an important perspective for understanding the long-term development of AI infrastructure.

Looking Ahead: Beta Is Focused Not Only on the Scale of Computing Power, but Also on Its Practical Value

From the continued growth of Microsoft’s cloud services and demand for SK hynix’s high-performance storage products to the expansion of Vertiv’s data center infrastructure business, the results announced by these international companies in October 2026 provide a clear view of the AI industry’s development.

They demonstrate that the growth of the AI industry is affecting multiple areas, including computing, storage, and data center infrastructure.

At the same time, the industry must address practical issues such as energy supply, equipment costs, technological development, resource utilization, and returns on investment.

In the future, the most important consideration will not simply be how many data centers are constructed or how much computing equipment is deployed worldwide. It will be whether these resources can be organized effectively, used efficiently, and continuously applied to genuine industrial requirements.

For Beta Creative Technology, continuing to monitor industry developments across AI Infrastructure, AI Applications, and AI Services, understanding the relationships between different parts of the value chain, and advancing its business on the basis of genuine customer demand and verifiable operating performance will support a clearer understanding of the long-term value of AI infrastructure.

Computing power provides the foundation. Infrastructure supports operations. Applications create value. Continuous services connect the future.

As artificial intelligence becomes further integrated into industry and everyday life, complete computing infrastructure systems and the collaborative capabilities behind them will remain important areas of industry development.

BETA CREATIVE TECHNOLOGY

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