An article written by Microsoft CEO Satya Nadella.

An article written by Microsoft's CEO Satya Nadella,
This kind of article is truly worth reading; don't focus on most AI articles written by the press.

Satya:

I have been contemplating the future development direction of companies in an AI-driven economic environment.

This transformation is fundamentally different from any platform change before. In the past, we used digital systems to enhance human capital. Now, for the first time, we can establish a true cognitive feedback loop between humans and digital systems. This is refreshing because it fundamentally changes our understanding of internal enterprise work.

The key is not about certain digital tools or systems and their usage, but about how organizations continue to learn, build intellectual property, differentiate themselves, and thrive in a world where AI models can continuously absorb human and organizational expertise and commodify it.

Every company must build what I call human capital and token capital. Human capital includes employees' knowledge, judgment, networks, creativity, and pattern recognition abilities, while token capital is the AI capabilities that the company develops and owns.

Importantly, as token capital grows, the value of human capital does not decrease; it only increases! I believe human initiative will be the driving force behind the growth of token capital. Humans will set ambitious goals, connect information across different fields, build relationships, and identify the most important patterns. Without human guidance, computers will spin in place.
This means the real opportunity is not about choosing the best model but about building a model-based learning cycle that allows human capital and token capital to grow exponentially. You can outsource a task or even a job, but you can never outsource learning. The future of enterprises depends on whether they can achieve compound growth of this learning output between personnel and AI.

This requires a completely new architectural approach, enabling each enterprise to build intelligent systems that improve over time while maintaining control over their intellectual property. Companies should be able to replace existing “general” models without losing the embedded expertise of their “company veterans” within their learning systems. This will be a key “test” of control and autonomy for enterprises in the future era.

Companies need to transform their workflows, domain knowledge, and accumulated judgment into AI systems that continuously improve with each use. Private evaluation should be able to detect whether the model truly improves results that are critical to the business (not just external benchmarks!). Private reinforcement learning environments should allow models to grow based on real internal data. Their knowledge bases enable organizational memory to be queryable and improve token efficiency.

This cycle will become the company's new intellectual property. I compare it to a mountain-climbing machine. Unlike most assets, it has a compounding effect. Each workflow improvement generates better training signals, accelerating the accumulation of the company's unique tacit knowledge. Companies that build this cycle early will have an inimitable advantage, regardless of what new single-model capabilities they possess.

What we least want to see is all industries and companies handing over value to a few models that dominate everything. If all value concentrates in a handful of models, the political and economic systems will absolutely not tolerate it. Society will never allow the future of AI to hollow out entire industries.

Think about what happened during the first phase of globalization—outsourcing drained entire industrial economies. On the surface, GDP data looked good, but industry transfer was real, and its consequences are still evident today. We must not let this pattern repeat in the AI era, where a few AI systems capture all economic benefits while entire industries watch their knowledge commodified and ultimately destroyed.

I believe our top priority must be to build an advanced ecosystem, not just a cutting-edge model, so that value can flow broadly to every company, industry, and country. In this ecosystem, every organization should be able to own a learning cycle that encodes its institutional knowledge, continuously accumulating human capital and token capital.

I have always believed that platforms can create more value than what they themselves provide, and every company can continuously innovate to create its own value.

When this happens, enterprises will not only generate value for themselves but also for the surrounding economy. Employees’ expertise will be enhanced, their judgment integrated into replicable, scalable systems, and both companies and the broader community will benefit.

This is how companies can create value for themselves and the wider economy. And this is the stable balance we should jointly build.