Headline news says a story: Openai, Meta, Google and Anthropic are in a military reserve competition to build the most powerful AI model. Each new version (from the open source model of Deepseek to the latest GPT update) is regarded as the next huge leap of AI and entered its fate. This means obvious: AI’s future belongs to people who establish the best model.
That’s the wrong way to look at it.
Companies that develop AI models are not alone to define its influence. AI supports the real participants in batches of adoption. It is not OpenAI or Meta, but a large score, data center operator and energy provider, making AI a growing consumer foundation. Without them, artificial intelligence is not a trillion -dollar industry. It is just the code sitting on the server, waiting for the unwilling power, calculation and cooling. Infrastructure rather than algorithms will determine how AI can use its potential.
The growth of AI and the efforts of infrastructure
AI will be disconnected from reality. The adoption rate of AI is accelerating, but it occupies the foundation under a simple limit: we have no ability, data center or cooling capabilities to support it in the scale of the industry’s expectations.
This is not guess, it is already happening. AI workload is fundamentally different from traditional cloud computing. The calculation intensity is higher in order, which requires special hardware, high -density data centers, and cooling systems that improve efficiency limit.
The company and the government not only run a AI model, but also run thousands of models. Military defense, financial services, logistics, manufacturing-every department is training and deployment of AI models customized according to their specific needs. This creates the spread of AI. The models are not concentrated, but are scattered across the industry. Each industry requires a lot of computing and infrastructure investment.
Unlike traditional corporate software, AI not only has to develop costs, but also runs expensive. The infrastructure required for AI models is exponentially increased according to the infrastructure required for large -scale operation. Each new deployment will increase pressure to the tight system.
The least technology in artificial intelligence
The data center is the real backbone of the AI industry. Each inquiry, each training cycle, each inference depends on the data center that has power, cooling and calculation to process.
Data centers have always been crucial to modern technology, but AI will increase index. A single large -scale AI deployment can consume the same power as medium -sized cities. The energy consumption and cooling of AI specific data centers are designed far more than the traditional Yunji infrastructure.
The company has fallen into restrictions:
- The position of the data center is now determined by power availability.
- High standards are not only built near the Internet skeleton, but they can obtain stable energy supply here.
- Cooling innovation becomes crucial. Line cooling,
- The energy efficiency system of immersion cooling and AI driver is not just good products-they are the only way for data centers to keep up with demand.
- The cost of artificial intelligence infrastructure is becoming differentiated.
- Those who figure out how to expand the cost efficiency of AI (no need to raise their energy budget) will dominate the stage of the next AI.
The reason for AWS, Microsoft and Google is the reason for one reason to invest billions of dollars into the AI-Ready infrastructure because without it, AI will not expand.
The future AI superpower
Artificial intelligence is already a national security issue, and the government is not outside. Today, the largest AI investment comes from consumer AI products, but also from national defense budgets, intelligence agencies and national scale infrastructure projects.
Military applications alone will require tens of thousands of private persons, closed AI models, and each model requires a safe and isolated computing environment. AI is built for everything from missile defense to supply chain logistics to threat detection. These models will not be open source, freely used systems. They will be locked, highly professional, and depend on large -scale computing power.
The government is ensuring long -term AI energy in the way of fixed oil and rare earth minerals in history. The reason is simple: large -scale AI requires large -scale energy and infrastructure.
At the same time, high standards position themselves as AI landlords. Companies like AWS, Google Cloud and Microsoft Azure are no longer just cloud providers-they are the goalkeepers of the infrastructure, they decide who can expand AI and who cannot expand.
This is why companies training AI models also invest in their infrastructure and power generation. Openai, humans and Meta depend on today’s cloud high standards, but they are also building a bottle of bottle bottle by the establishment of the AI cluster that will not be maintained by my third -party infrastructure. Long -term winners in AI are not only the best model developers, they will become capable of being able to afford, operate and maintain large -scale infrastructure AI who truly change the large -scale infrastructure required for the game.
