Written by: Satya Nadella, @satyanadella
Compiled by: Gandalf, Techub News
The economic landscape and future prospects of enterprises driven by artificial intelligence have always been core issues that I think about deeply.
This transformation is unlike any previous platform revolution. In the past, digital systems were merely tools for enhancing human capital. Now, for the first time, we are establishing a true cognitive loop between people and digital systems. This profound change is even reshaping our fundamental understanding of internal enterprise collaboration models.
The core challenge is not the application of digital tools or systems themselves, but rather how organizations can achieve continuous learning, build intellectual property, differentiate themselves, and thrive in the current global landscape. This is a world where AI models continuously absorb and commodify human and organizational expertise.
Every company must build what I call a dual-engine system of human capital and tokenized capital. Human capital encompasses employees' knowledge reserves, judgment, networks, creativity, and pattern recognition abilities. Tokenized capital, on the other hand, refers to the AI capabilities that the company independently develops and masters.
The key is that as token capital expands, the value of human capital increases rather than decreases. I firmly believe that human initiative will be the core driving force behind token capital growth. Humans set ambitious goals, connect cross-domain clues, build deep relationships, and identify key patterns. Without human guidance, computing resources will simply stagnate and be ineffective.
This means the real opportunity lies not in selecting the optimal model, but in building a learning loop on top of that model to achieve compound growth in both human and tokenized capital. Tasks and even positions can be outsourced or transferred, but learning itself cannot be transferred. The key to the future vision of enterprises lies in the compounding effect of learning between humans and AI.
This requires a completely new architectural paradigm. Under this paradigm, enterprises can build intelligent agent systems that evolve over time while maintaining firm control over intellectual property. Enterprises should have the ability to replace "general" models without losing the "organizational expert knowledge" assets built into their learning systems. This is a crucial litmus test for control and digital sovereignty in the future.
Enterprises must transform their workflows, domain expertise, and accumulated judgment into AI systems that become increasingly intelligent with use. Privatization evaluation systems should accurately capture the real improvements that models bring to key business metrics. This system should not merely benchmark against external standards. Privatization reinforcement learning environments should allow models to continuously evolve based on the organization's real-world trajectory. Knowledge base construction should enable the retrieval and querying of institutional memories and improve the efficiency of token capital utilization.
This closed loop constitutes a new type of intellectual property for enterprises. I liken it to a mountaineering engine. Unlike most assets, it generates a compounding effect. Each optimization of the workflow generates higher-quality training signals, thereby accelerating the accumulation of the enterprise's unique tacit knowledge. Enterprises that first build this system will gain a competitive barrier that is difficult to replicate. This advantage will persist regardless of subsequent breakthroughs in the capabilities of individual models.
What we least want to see is an industry landscape where companies across all sectors continuously cede value to a few models, which then devour everything they touch. If all value is captured by only a few models, the political and economic order simply cannot tolerate this situation. Society will never allow an AI future that hollows out the foundations of any industry.
Looking back at the first phase of globalization, the entire industrial economy was hollowed out by outsourcing. While GDP figures appeared impressive on the surface, the pain of displacement and loss of livelihoods was real, and its repercussions are still felt today. We should not carry this imbalance into the AI era: a few AI systems reaping all the economic rewards while the entire industry watches helplessly as its knowledge is completely commodified.
In my view, our priority must be building a cutting-edge technology ecosystem, rather than simply creating a single cutting-edge model, so that value can circulate widely to every company, every industry, and even every country. In this ecosystem, each organization can have its own proprietary learning loop mechanism: this loop encodes its institutional knowledge and enables its human capital and token capital to achieve continuous compound growth.
This is a philosophy I've always held throughout my career. According to this philosophy, the value a platform creates on the surface of the network should far outweigh the portion it extracts internally; and each company can continuously innovate and build its own unique value.
When this vision becomes a reality, the company will create value for itself and the surrounding economic ecosystem. Employees will witness their professional skills amplified, and their professional judgment will become part of the system. These systems will make their judgment replicable and scalable, and the benefits will be accumulating within their company and community.
This is precisely the fundamental path for companies to create value for themselves and the wider economy. This is also the stable and balanced structure we should work together to establish.
