The artificial intelligence industry is entering a new stage.
Over the past few years, market attention around AI has focused more on large models, algorithmic capabilities, parameter scale, and application innovation. Companies with stronger models, faster products, and more imaginative applications have often found it easier to attract capital and market attention.
But as AI moves from technical demonstration to industrial application, a more practical question is emerging: what is needed to support model capabilities?
The answer is becoming increasingly clear. It is computing power, data centers, high-performance chips, stable energy, network transmission, cooling systems, and digital infrastructure capable of continuously supporting AI training, inference, and industry deployment.
This means the competitive logic of the AI industry is changing. From model racing to computing foundations; from technological breakthroughs to industrial coordination; from short-term market themes to long-term asset revaluation, AI is moving from a “light-asset narrative” into a “heavy-asset era.”
In the view of SHINDEV’s investment research team, this shift will profoundly affect how capital markets assess value in the future. Capital is looking again for long-term value across the AI industrial chain. It is no longer focused only on individual model companies, but increasingly on who can command infrastructure capabilities, who can connect technology with real industrial scenarios, and who can form sustainable asset value in the restructuring of the AI industry.
Early AI competition was mainly concentrated at the technical level. Model architecture, algorithmic capabilities, training data, and product experience were important indicators for the market to evaluate AI companies.
But as AI begins to enter enterprise operations, financial services, industrial manufacturing, medical research and development, energy dispatching, and urban governance, the focus of competition is extending further downward. Large models require training computing power for continuous iteration. AI applications require inference computing power for large-scale use. True industrial intelligence requires stable, callable, and scalable infrastructure capabilities.
The International Energy Agency (IEA) stated in its Energy and AI report that global data center electricity consumption was about 415 TWh in 2024, accounting for around 1.5% of global electricity consumption. By 2030, global data center electricity consumption is expected to reach around 945 TWh, nearly doubling and accounting for close to 3% of global electricity demand. Among this, electricity demand from AI-optimized data centers is growing even faster and is expected to increase more than fourfold by 2030.
This data shows that the expansion of the AI industry is no longer only software-level growth. It is directly driving rising demand for foundational resources such as electricity, data centers, chips, servers, cooling equipment, and network facilities.
In other words, AI is no longer just a competition of “writing code” and “training models.” It has entered a competition of infrastructure capabilities.
As AI moves toward large-scale application, computing power is becoming a new foundational asset.
Gartner’s forecast released in May 2026 shows that global AI spending is expected to reach USD 2.59 trillion in 2026, up 47% year on year. AI infrastructure spending is expected to reach USD 1.43 trillion, accounting for more than 45% of total AI spending and making it the largest market segment.
Why is capital flowing into infrastructure? Because the more mature the AI industry becomes, the more dependent it is on underlying resources.
A data center is not simply a machine room. It is a complex project integrating servers, GPUs, network equipment, power systems, cooling systems, storage systems, security systems, and operations and maintenance capabilities. Computing power is not a single device capability either. It involves chip supply, cluster management, energy costs, scheduling efficiency, and matching customer demand.
This trend can also be seen in the performance of global technology leaders. NVIDIA’s fiscal 2026 revenue reached USD 215.9 billion, up 65% year on year. Its fourth-quarter data center revenue reached USD 62.3 billion, up 75% year on year. Behind this growth is the continued pull of global AI training, inference, and cloud computing demand for high-performance computing power.
In the view of SHINDEV’s investment research team, computing infrastructure is developing asset attributes similar to electricity, communications, and transportation networks. It serves not only technology companies, but also finance, manufacturing, healthcare, energy, transportation, scientific research, and a broader range of industries. It has both growth potential and the characteristics of long-term operation and asset accumulation.
This is an important reason why capital is repricing the AI industrial chain.
In the past, AI investment more easily revolved around model companies, algorithm teams, and application products. But after entering the heavy-asset era, capital’s focus is changing.
What truly deserves long-term attention is not only individual model companies, but industrial ecosystems that can connect technology, computing power, and real industrial scenarios.
McKinsey’s 2026 research indicates that, driven by AI demand, global data center demand could grow from around 82 GW in 2025 to around 220 GW by 2030, nearly tripling. By 2030, global data center capital expenditure demand could reach around USD 6.7 trillion, of which AI-related data center capital expenditure could be around USD 5.2 trillion.
This means opportunities across the AI industrial chain are spreading from the model layer to the infrastructure layer, energy layer, equipment layer, and industrial application layer.
For capital, the standards for judging the value of AI companies also need to change. It cannot look only at model parameters, financing momentum, and short-term user growth. It must also examine whether a company has access to real industrial scenarios, whether it can form a stable business model, whether it has computing power scheduling and delivery capabilities, and whether it can improve efficiency, reduce costs, and create sustained revenue in specific industries.
The long-term value of AI will not remain only in technological leadership itself. It will be reflected in industrial chain coordination capabilities.
AI infrastructure construction has a long cycle, requires large investment, and faces strong resource constraints. It is not suitable to be judged entirely with a short-term market theme mindset.
A data center project often requires a long cycle from site selection, construction, power access, equipment procurement, and network deployment to customer onboarding. Returns on computing infrastructure cannot be realized through one round of market sentiment. They depend on long-term customer demand, continuous operations and maintenance capabilities, and stable cash flow.
This is why long-term capital is becoming a key variable in the development of the AI industry.
At this stage, the role of capital is not only to “invest money,” but also to participate in industrial organization. It needs to help companies connect computing power, energy, equipment, customers, application scenarios, and financial tools. It also needs to support a more stable development structure for the AI industrial chain through industry funds, project financing, mergers and acquisitions, and infrastructure asset allocation.
Especially as energy constraints become more prominent, green computing power is also becoming an important direction. The IEA has noted that future additional electricity demand from data centers will be supported by a mix of renewable energy, natural gas, nuclear power, and other sources. For AI infrastructure projects, power access, energy costs, carbon emissions management, and cooling efficiency will all affect long-term competitiveness.
This shows that the AI industry is no longer only a technology issue. It is also an issue of capital, energy, and industrial coordination.
From the perspective of financial capital, SHINDEV’s investment research team believes that the AI industry is undergoing a deep shift from technological innovation to industrial restructuring.
First, the underlying logic of AI competition is shifting from model capability to infrastructure capability. In the future, whoever can obtain stable, low-cost, and scalable computing resources will be more likely to take the initiative in industrial competition.
Second, computing infrastructure is becoming a new type of asset in the intelligent era. Data centers, chips, servers, electricity, cooling, networks, and cloud platforms will jointly form the foundation for large-scale AI development and become important directions for long-term capital allocation.
Third, AI investment is moving from “investing in technology” to “investing in industry.” Single-point technological breakthroughs remain important, but what truly has long-term value is the enterprises and ecosystems that can connect technological capabilities, computing resources, and industrial scenarios.
Fourth, long-term capital will become an important support for AI infrastructure construction. After AI enters the heavy-asset era, capital needs to shift from short-term theme judgment to industrial-cycle judgment, accompanying industrial growth with longer-term capital and resource allocation capabilities.
Looking ahead, SHINDEV will continue to focus on the new assets, new industries, and new value chains formed as AI moves from technological innovation to industrial restructuring. Around computing infrastructure, green computing power, industrial intelligence, technology finance, and digital infrastructure, SHINDEV will continue to carry out investment research observation and industrial analysis.
The first half of AI was a race in models. The second half will be a comprehensive competition among computing foundations, industrial coordination, and capital efficiency.
As AI enters the heavy-asset era, capital markets are looking again for industrial value that can truly endure cycles. For SHINDEV, what matters is not only seeing the technological wave of AI, but also understanding the infrastructure restructuring, asset value revaluation, and long-term industrial opportunities behind that wave.
