Written by: Satya Nadella, @satyanadella

Translated by: Gandalf, Techub News

The economic landscape driven by artificial intelligence and the future of enterprises has always been the core topic of my deep thinking.

This transformation is fundamentally different from any previous platform changes. In the past, digital systems merely served as enhancement tools for human capital. Today, we are establishing a real cognitive feedback loop between people and digital systems for the first time. This transformation is so profound that it even reshapes our underlying understanding of collaboration models within enterprises.

The core contradiction is not the application of digital tools or systems themselves, but how organizations achieve continuous learning, intellectual property construction, differentiated competition, and vigorous development under the global landscape. This is a world where AI models continuously absorb human and organizational expertise and commercialize it.

Every enterprise must build what I call a dual-wheel system of human capital and token capital. Human capital encompasses the knowledge reserves, judgment, networks, creativity, and pattern recognition capabilities of employees. Token capital is the AI capability assets developed and controlled by the enterprise.

The key is that as token capital expands, the value of human capital increases rather than decreases. I firmly believe that human initiative will become the core driving force behind the growth of token capital. Humans set grand goals, connect cross-domain clues, establish deep relationships, and recognize key patterns. Without human guidance, computational resources will only spin ineffective.

This means that true opportunity does not lie in selecting the optimal model, but in building a learning feedback loop on top of models to achieve compound growth of human capital and token capital. Tasks and even positions can be outsourced, but the act of learning itself cannot be transferred. The key to the future of enterprises lies in the compounding learning effect formed between humans and AI.

This requires a completely new paradigm. Under this paradigm, enterprises can construct intelligent agent systems that evolve over time, while firmly controlling intellectual property. Enterprises should have the ability to replace "general" models without losing the embedded "organizational expert knowledge" assets in the learning system. This is a key touchstone for control and digital sovereignty in the future era.

Enterprises must transform workflows, domain expertise, and accumulated judgment into increasingly intelligent AI systems. The privatized evaluation system should accurately capture the real improvements that models bring to key business indicators. This system should not only benchmark against external standards. The privatized reinforcement learning environment should allow models to continuously evolve based on the true trajectories within the organization. Knowledge base construction should make institutional memory retrievable and enhance the utilization efficiency of token capital.

This feedback loop constitutes a new type of intellectual property for enterprises. I liken it to a mountain climbing engine. Unlike most assets, it can produce a compounding effect. Every optimization of a workflow generates higher quality training signals, which in turn accelerates the sedimentation of the enterprise's unique tacit knowledge. Enterprises that first build this system will gain an irreplicable competitive barrier. Regardless of how subsequent single model capabilities breakthrough, this advantage will persist.

What we least want to see is a scenario where companies across various industries continue to transfer value to a few models, while these models consume everything they touch. If all value is solely captured by a few models, the political and economic landscape absolutely cannot tolerate such a situation. Society will never allow an AI future that hollow out the foundation of industries.

Looking back at the first phase of globalization: the entire industrial economic system was hollowed out due to outsourcing. Although GDP data appeared bright on the surface, the pain of dislocation for the populace was real, and its aftershocks can still be felt today. We should not bring this unbalanced situation into the AI era: few AI systems capturing all economic returns while the entire industry watches helplessly as its knowledge is completely commodified.

In my view, our top priority must be to build a cutting-edge technology ecosystem, rather than merely creating a single cutting-edge model, so that value circulates widely to every company, every industry, and every country. In this ecosystem, every organization can have its exclusive learning feedback loop mechanism: this loop will encode its institutional knowledge and enable its human capital and token capital to achieve continuous compound growth.

This has always been the principle I adhere to in my growth. According to this principle, the value created by the platform at the surface network should far exceed the portion it captures internally; every company can also continue to innovate and build its unique value.

When this vision becomes a reality, companies will create value for themselves and their surrounding economic ecosystems. Employees will witness their professional skills being amplified, and their professional judgment will also become a part of the system. These systems make their judgment replicable, scalable, and will deposit benefits into the companies and communities they belong to.

This is precisely the fundamental path for companies to create value for themselves and the broader economy. It is also the stable equilibrium pattern we should collectively establish.

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