Author: Satya Nadella , Microsoft CEO
Compiled by: Yuliya, PANews
Editor's Note: This article is a translation of the latest insights from Microsoft CEO Satya Nadella. In it, Nadella explores the future development of businesses in an AI-driven economy. He points out that as AI models continue to evolve, the core challenge for businesses is no longer simply which digital tools to use, but how to build and accumulate "human capital" and "token capital" representing their unique AI capabilities. He emphasizes that the intervention of AI will not devalue people; on the contrary, it will generate a powerful compounding effect due to human initiative. At the same time, Nadella warns that we cannot repeat the mistakes of industrial hollowing out in the early stages of globalization, allowing a few AI models to devour all value. Instead, society as a whole should work together to build a thriving "cutting-edge ecosystem," enabling every organization to control its intellectual property and learning cycle, thereby achieving true and lasting win-win results in the AI era. The following is the translated text:
Without an ecosystem to support it, no cutting-edge technology can stand firm.
Recently, I've been deeply reflecting on the future of businesses in the AI-driven economy.
This transformation is unlike any previous platform shift. In the past, we used digital systems to enhance human capital. Now, for the first time, we are able to establish a true cognitive loop between humans and digital systems. This is fundamentally disruptive because it completely changes our fundamental definition of "work" within the enterprise.
The real challenge is no longer how a particular digital tool or system is used, but how organizations will continue to learn, build intellectual property, differentiate themselves, and thrive as AI models can continuously absorb and commoditize human and organizational expertise.
Every company must begin building what I call “human capital” and “token capital.” Human capital includes employees’ knowledge, judgment, interpersonal skills, creativity, and pattern recognition abilities; while token capital refers to the proprietary AI capabilities that a company builds and owns.
It's important to emphasize that human capital will not depreciate as token capital grows. On the contrary, it will only become more valuable! I believe that human initiative will be the core driving force behind token capital growth. Humans will set ambitious goals, build connections across different fields, expand their networks, and identify the most critical patterns. Without human guidance, computing power will simply stagnate.
This means the real opportunity lies not in selecting the best model, but in building a learning cycle on top of that model, allowing human capital and token capital to generate a compounding effect. You can outsource a task or even a job, but you can never outsource your "learning" ability. The future of businesses lies in this ability to continuously accumulate and amplify this learning cycle between humans and AI.
This requires a completely new architectural approach: enabling each enterprise to build intelligent agent systems that continuously improve over time while maintaining firm control over its intellectual property. A company should be able to replace a "generic" model at any time without losing the "veteran" expertise accumulated in its learning system. In the future, this will be a crucial "test" of whether you truly possess control and digital sovereignty.
Enterprises need to transform their business processes, domain knowledge, and accumulated judgment into AI systems that improve with each use. Private domain assessments should accurately capture whether the model is truly improving on business outcomes critical to the enterprise (not just looking at external benchmark results!). Private reinforcement learning environments should allow the model to become increasingly powerful within the organization's real-world data trajectory. Such a knowledge base makes internal policy memories searchable and makes token usage more efficient.
This closed-loop cycle will become a brand new IP for the company. I liken it to a "climbing machine." And unlike most assets, it has a compounding effect. Each improved workflow generates better training signals, thereby accelerating the accumulation of the company's unique tacit knowledge. No matter what new independent model capabilities emerge, companies that build this closed loop early will have a competitive advantage that is difficult to replicate.
The last thing any of us wants to see is a world where every company in every industry is handing over value to a few all-consuming models. If all value is concentrated in the hands of only a tiny minority of models, the political and economic system simply cannot tolerate it. Society will never allow an AI future that hollows out the entire industry.
Reflect on what happened during the first phase of globalization: outsourcing severely hollowed out the entire industrial economy. The GDP figures looked impressive on the surface, but the pain of people losing their jobs was real, and its consequences continue to resonate today. We must not allow this dynamic to repeat itself in the AI era—we cannot allow a few AI systems to reap all the economic rewards while we watch as the knowledge of entire industries is unknowingly and cheaply commodified.
In my view, our top priority must be building a cutting-edge ecosystem, not just developing a cutting-edge model. Only in this way can value flow widely to every company, every industry, and every country. In this ecosystem, every organization can have a learning cycle that carries its institutional knowledge, allowing its human capital and token capital to continuously generate compound interest.
This is the philosophy I have always upheld throughout my life: the external value generated by a platform should far exceed the value it retains, and every company should be able to continuously innovate and create its own value on the platform.
When all this becomes a reality, businesses will not only create value for themselves but also inject vitality into the surrounding economy. Employees will see their professional skills amplified, their judgment integrated into the system, becoming replicable and scalable, and the resulting benefits will be returned to their businesses and communities.
This is precisely how businesses create value for themselves and the wider economy. This is also the stable balance we should work together to build.
