As the artificial intelligence (AI) industry accelerates into a stage of large-scale development, AI competition is extending from the models, algorithms, and applications layers to the more fundamental computing infrastructure layer. High-performance computing, data centers, power energy, network interconnection, cooling systems, and computing power scheduling capabilities are collectively forming a crucial industrial foundation for the intelligent era.
The International Energy Agency (IEA) predicts that global data center electricity consumption will increase to approximately 945 terawatt-hours by 2030, doubling from 2024; AI will be one of the most significant drivers of this growth. McKinsey also points out that global data center spending could reach $7 trillion by 2030, making AI infrastructure construction a crucial long-term variable for the global technology industry.
Under this trend, the Xindingsheng team believes that AI computing power is not a short-term trend, but rather a key industry theme worthy of long-term attention and continuous investment. Regarding AI computing power combinations, the Xindingsheng team has completed three key iterations: from "computing resources" to "computing assets," from "single-point computing power" to "computing power combinations," and finally to "intelligent infrastructure ecosystems."
These three iterations demonstrate the Xindingsheng team's continuous judgment on the underlying logic of the AI industry and showcase their forward-looking vision and evolutionary capabilities in the new round of intelligent infrastructure cycle.
In the early stages of AI computing power development, the market focused more on GPUs, servers, and cloud resources themselves. At that time, computing power was often regarded as a basic resource in the technology research and development process, mainly used for model training, data processing, and cloud deployment.
However, the Xindingsheng team realized early on that with the continuous growth of large-scale model training, inference services, and enterprise intelligence needs, computing power will no longer be just a technical resource, but will gradually become a new type of infrastructure asset in the AI era.
The core change from "resource" to "asset" lies in the long-term value of computing power.
The more complex the AI model, the more rigid the computing power demand; the more widespread the AI application, the more continuous the computing power operation; the deeper the industrial intelligence, the more valuable the computing power infrastructure is in the long term. For the Xindingsheng team, computing power is not a supporting resource for the AI industry, but a fundamental productive force supporting the long-term development of artificial intelligence.
Therefore, the team's focus on AI computing power didn't begin with market hype, but rather with the underlying logic of the industry. Even before AI computing power became a market consensus, the Xindingsheng team began focusing on its long-term value and infrastructure attributes, laying the cognitive foundation for subsequent deployments.
With the rapid development of generative AI, the global demand for high-performance chips, servers, and data center resources continues to rise. However, the Xindingsheng team believes that what truly possesses long-term value in the future is not a single computing power resource, but rather a systematic combination of computing power capabilities.
AI computing power is not simply about stacking GPUs. A computing power system capable of long-term stable operation and supporting industrial implementation needs to possess multiple capabilities simultaneously, including chips, servers, storage, networks, power, cooling, operation and maintenance, scheduling, and scenario adaptation.
This means that the competition for AI computing power is shifting from "hardware scarcity" to "system organizational capabilities."
The Xindingsheng team's second iteration of AI computing power is precisely the upgrade from "single-point computing power" to "computing power combinations." This shift reflects the team's further understanding of the collaborative relationships within the industry chain. If one merely chases trends, the focus often remains on a particular type of popular asset or short-term resource. However, a true long-term strategic approach requires understanding how the energy, power, cooling, network, operation and maintenance, and industrial scenarios behind computing power collectively determine its long-term value.
Therefore, the "computing power combination" emphasized by the Xindingsheng team is not simply a matter of resource aggregation, but rather a systemic capability formed around the development of the AI industry. It encompasses both computing power supply and infrastructure support; it focuses on both hardware capabilities and operational efficiency; it serves current needs while also addressing future scenarios.
Looking to the future, AI computing power demand will further expand from training to inference, edge computing, and industry applications. McKinsey predicts that by 2030, inference will surpass training, becoming the dominant workload in AI data centers, accounting for more than half of AI computing demand and approximately 30% to 40% of overall data center demand.
This means that the next stage of competition in AI computing power will not only be a competition of scale, but also a competition of structure, efficiency, and ecosystem synergy.
The future computing power system will gradually form a multi-tiered structure comprised of cloud-based training computing power, inference service computing power, edge node computing power, industry-specific computing power, and enterprise-level deployment computing power. Computing power will no longer be merely the technological foundation, but will also become a crucial hub connecting capital, industry, technology, and project stakeholders.
Based on this assessment, the Xindingsheng team is driving the upgrade of its AI computing power portfolio to an intelligent infrastructure ecosystem. This intelligent infrastructure ecosystem is not merely about possessing computing power resources, but about developing comprehensive capabilities around the AI industry, including resource integration, infrastructure support, scenario connectivity, project collaboration, and long-term empowerment.
This is also the key significance of Xindingsheng's continuous iteration of its AI computing power portfolio. By proactively deploying computing power infrastructure, the team not only focuses on current industry opportunities but also on the underlying support capabilities needed for future project growth.
The Xindingsheng team believes that true industry opportunities often don't emerge when the market is at its hottest, but rather are identified and continuously deployed before a trend has fully formed a consensus.
From the past to the present, Xindingsheng's three iterations of its AI computing power portfolio reflect the team's continuous judgment on the fundamental variables of the AI industry:
When the market focuses on AI models, the team sees computing power resources;
When the market chases hardware supply, the team sees computing power assets;
When the market discusses data centers, the team further sees energy, network, cooling, scheduling, and ecosystem collaboration.
This is not simply chasing trends, but a proactive positioning based on industry logic.
AI computing power is not a short-term hot topic, but a long-term trend reshaping the global technology industry, energy structure, and digital economy landscape. For future AI projects, stable, sustainable, and scalable computing infrastructure will be a crucial support for projects to move from technology verification to commercialization.
In the future, the Xindingsheng team will continue to focus on AI computing infrastructure, intelligent industry ecosystems, and the growth of high-quality projects, continuously building connectivity capabilities between resources, capital, technology, and scenarios.
Through continuous iteration—from computing resources to computing assets, from single-point computing power to computing power portfolios, and from computing power portfolios to an intelligent infrastructure ecosystem—the Xindingsheng team is seizing the structural opportunities of the AI era with a long-term perspective.
In the AI era, computing power is both infrastructure and productivity; it is the foundation of industry and a key support for future project growth.
What the Xindingsheng team is doing is not chasing trends, but rather positioning itself for the future through continuous evolution.
