An article written by Microsoft CEO Satya Nadella,

An article written by Microsoft CEO Satya Nadella:

This kind of article is actually worth reading—don’t go look at most of the AI-written content from China-centered circles.

Satya:

I’ve been thinking about the direction the company should take in the future in an economy driven by artificial intelligence.

This transformation is completely different from any prior platform shift. In the past, we used digital systems to enhance human capital. And now, for the first time, we can build a true cognitive loop between people and digital systems. It’s refreshing, because it fundamentally changes how we understand work inside enterprises.

The key isn’t certain digital tools or systems and how to use them. In a world where AI models can continuously absorb human and organizational expertise and commercialize it, the real question is how organizations continue learning, build intellectual property, achieve differentiation, and thrive.

Every company must build what I call human capital and token capital. Human capital includes employees’ knowledge, judgment, network relationships, creativity, and pattern-recognition ability—while token capital is the AI capability a company builds and owns.

What matters is that as token capital grows, the value of human capital doesn’t decrease—it increases! I believe people’s agency will be the driving force behind the growth of token capital. Humans will set ambitious goals, connect information across different domains, build networks, and identify the most important patterns. Without human guidance, computers will just spin in place.

This means the real opportunity isn’t choosing the best model—it’s building a model-based learning loop that allows human capital and token capital to compound over time. You can outsource a task, or even a job, but you can never outsource learning. The future of enterprises lies in whether they can compound the learning outcomes that grow between people and artificial intelligence.

This requires an entirely new architectural approach—one that enables each enterprise to build intelligent systems that improve over time while still retaining control over its intellectual property. Companies should be able to replace existing “general” models without losing the “company veterans” expertise embedded in their learning systems. This will be the key “test” for corporate control and autonomy in the future era.

Enterprises need to convert their workflows, domain knowledge, and accumulated judgments into AI systems—and make them improve with every use. Private evaluations should be able to capture whether the model is truly improving outcomes that are critical to the business (not just external benchmarks!). Private reinforcement learning environments should allow models to grow continuously based on real data inside the organization. Its knowledge base makes institutional memory queryable and improves the efficiency of token usage.

This loop will become the company’s new intellectual property. I compare it to a mountain-climbing machine. Unlike most assets, it has a compounding effect. Every improvement to a workflow creates better training signals, accelerating the accumulation of the company’s unique, tacit knowledge. Companies that build this loop early will have advantages that are difficult to replicate—regardless of whatever new single-model capabilities they may have.

What we least want to see is that all industries, all companies, hand their value over to a few models that seize everything. If all value concentrates in the hands of a few models, the political-economic system will absolutely not tolerate it. Society will never allow the future of artificial intelligence to hollow out an entire industry.

Think about what happened in the first phase of globalization—outsourcing hollowed out entire industrial economies. On the surface, GDP data looked fine, but the transfer of industries was real, and the consequences are still showing up today. We must not allow this pattern to repeat in the AI era—where a small number of AI systems capture all economic benefits while the entire industry watches its knowledge get commoditized and ultimately destroyed.

I believe our top priority must be to build a frontier ecosystem—not just a frontier model—so that value can flow widely across every company, every industry, and every country. In this ecosystem, each organization can own a learning loop encoded with its institutional knowledge, continuously accumulating its human capital and token capital.

Ever since I was young, I’ve held this belief: platforms can create more additional value than the value the platform itself provides—and every company can continuously innovate and create value of its own.

When this happens, enterprises can create value not only for themselves, but also for the surrounding economy. Employees’ expertise will be enhanced; their judgment will be integrated into replicable, scalable systems—and both the enterprise and the surrounding communities will benefit.

This is how enterprises create value for themselves and the wider economy. And this is the stable balance we should build together.