Latest article by Huang Renxun: AI's' Five Layer Cake ' Article: Huang Renxun Compilation: Peggy, BlockBeats Artificial intelligence is one of the most powerful forces shaping the world today. It is not a smart application or a single model, but an infrastructure, as important as electricity and the Internet. AI runs on real hardware, real energy, and real economic systems. It transforms raw materials into "intelligence" for large-scale production. Every company will use it, and every country will build it. To understand why AI unfolds in this way, it would be helpful to start from first principles and see what fundamental changes have occurred in the field of computing. From 'pre made software' to 'real-time generated intelligence', software has been 'pre made' for the vast majority of the history of computer development. Humans first describe an algorithm, and then computers execute it according to instructions. Data must be carefully structured, stored in tables, and retrieved through precise queries. SQL is indispensable because it enables this entire system to operate. AI breaks this pattern. For the first time, we have a computer that can understand unstructured information. It can view images, read text, listen to sound, and understand their meanings; It can infer context and intention. More importantly, it can generate intelligence in real-time. Every response is a new generation. Each answer depends on the context you provide. This is no longer software retrieving existing instructions from a database, but software inferring in real-time and generating intelligence on demand. Because intelligence is generated in real-time, the entire computing technology stack that supports it must also be re invented. If AI is viewed from an industrial perspective as an infrastructure, it can actually be broken down into a five layer structure. The bottom layer of energy is energy. Real time generated intelligence requires real-time generated electricity. The generation of each token means that electrons are moving, heat is being managed, and energy is being converted into computing power. Below this level, there is no abstraction. Energy is the first principle of AI infrastructure and the fundamental constraint that determines how much intelligence a system can produce. Chips are above the energy source. The design goal of these processors is to convert energy into computing power with extremely high efficiency under large-scale conditions. AI workloads require enormous parallel computing power, high bandwidth memory, and high-speed interconnection. The progress of the chip layer determines the speed of AI expansion and how cheap "intelligence" will ultimately become. On top of the infrastructure chip is the infrastructure. This includes land, power transmission, cooling systems, construction projects, network systems, and scheduling systems that organize tens of thousands of processors into a single machine. These systems are essentially AI factories. They are not designed to store information, but to create intelligence. On top of the Models infrastructure are the models. AI models can understand various types of information: language, biology, chemistry, physics, finance, medicine, as well as the real world itself. Language models are just one type among them. One of the most transformative jobs is happening in the following fields: protein AI, chemical AI, physical simulation, robotics, and autonomous system applications. The top layer is the application layer, where economic value is truly generated. For example, drug discovery platforms, industrial robots, legal Copilots, autonomous vehicle. A autonomous vehicle is essentially an "AI application carried by a machine"; A humanoid robot is an AI application carried by the body. The underlying technology stack is the same, but the final form presented is different. Therefore, this is the five layer structure of AI: energy → chip → infrastructure → model → application. Every successful application will drive down all levels, down to the power plant that supplies it at the lowest level. We have just begun the construction of an infrastructure project that is still in its early stages. The current investment scale is only a few hundred billion US dollars, and there is still a need to build infrastructure worth trillions of dollars in the future. On a global scale, we are seeing: chip factories, computer assembly plants, AI factories. Built on an unprecedented scale. This is becoming one of the largest infrastructure constructions in human history. The labor demand in the AI era supports the massive scale of labor required for this construction. AI factories require highly skilled and well paid positions such as electricians, plumbers, pipeline installers, steel structure workers, network technicians, equipment installers, and maintenance personnel, all of which are currently in extreme shortage. Participating in this transformation does not necessarily require a PhD in computer science. At the same time, AI is driving productivity improvements in the knowledge economy. Taking radiology as an example. AI has begun to assist in medical image interpretation, but the demand for radiologists is still growing. This is not contradictory. The true responsibility of a radiologist is to take care of patients, and reading X-rays is just one of them. As AI takes over more and more repetitive tasks, doctors can invest more time in judgment, communication, and treatment. The improvement of hospital efficiency can serve more patients, which requires more manpower. Productivity creates ability, ability creates growth. What changes have occurred in the past year? In the past year, AI has crossed a critical threshold. The model is already good enough to truly work in large-scale scenarios. For the first time, AI based applications have begun to create real economic value, with significantly improved reasoning abilities, significantly reduced hallucinations, and greatly enhanced grounding in the real world. There is already a clear product market match in the following fields: drug research and development, logistics, customer service, software development, manufacturing, and these applications are strongly driving the entire underlying technology stack. The role of open source models is crucial. The vast majority of AI models in the world are free. Researchers, startups, businesses, and even entire countries rely on open source models to compete in advanced AI. When open source models reach the forefront of technology, they not only change software, but also activate the requirements of the entire technology stack. DeepSeek-R1 is a typical example. By making a powerful inference model widely available, it has driven rapid growth in the application layer, while also increasing the demand for training computing power, infrastructure, chips, and energy. What does this mean? When you see AI as infrastructure, everything becomes clear. AI may have started with Transformers and large language models, but it's far more than that. It is an industrial level transformation that will reshape the production and consumption of energy, the construction of factories, the organization of work, and the mode of economic growth. The reason why AI factories are built is because intelligence can now be generated in real time. The reason why the chip was redesigned is that efficiency determines the speed of intelligent expansion. The reason why energy becomes the core is because it determines how much intelligence the system can produce at most. The reason why the application exploded is that the model finally crossed the threshold of "scale availability". Each layer is reinforcing the other layers. That's why this construction project is so massive, why it will affect so many industries simultaneously, and why it won't be limited to any one country or field. Every company will use AI. Every country will build AI. We are still in the early stages. A large amount of infrastructure has not yet been built, a large amount of labor has not been trained, and a large number of opportunities have not yet been realized. But the direction is already very clear. Artificial intelligence is becoming the fundamental infrastructure of the modern world. And the choices we make today, the speed of construction, the breadth of participation, and the responsibility of deployment, will determine what this era will ultimately become.