Original author: Satya Nadella, Microsoft CEO

Compiled by: Peggy

I have been thinking about what the future of businesses will look like in an AI-driven economy.

This transformation is unlike any previous platform migration. In the past, we used digital systems to enhance human capital; but this time, it is the first time we can truly establish a cognitive loop between people and digital systems. This is highly disruptive, as it will change our understanding of "work" within enterprises.

The real key issue is not how a particular digital tool or system is used, but how organizations can continue to learn, accumulate intellectual property, differentiate themselves, and thrive in a world where an AI model can continuously absorb human and organizational expertise and commercialize it.

Every company must build what I call human capital and token capital. Human capital includes the knowledge, judgment, networks, creativity, and pattern recognition abilities of employees; while token capital refers to the AI capabilities that the organization builds and owns.

Importantly, as token capital grows, human capital does not become less important. On the contrary, it will only become more crucial. I believe human agency will be the core driving force behind the growth of token capital. Humans will set ambitious goals, connect clues across domains, build relationships, and identify truly important patterns. Without human guidance, computing power will just go in circles.

This means that the real opportunity lies not in choosing the best model, but in creating a learning loop on top of the model, allowing human capital and token capital to compound each other. You can outsource a task, or even a job, but you can never outsource your own learning. The future of business lies in whether this learning can continue to compound between people and AI.

This requires a new architectural mindset: every enterprise should be able to build intelligent systems that continuously improve over time while still retaining control over its intellectual property. A company should be able to replace a "generalist" model without losing the "company veteran" type of expertise that is embedded in its learning system. This will be a critical test of corporate control and sovereignty in the future era.

Companies need to transform their workflows, domain knowledge, and long-accumulated judgment into AI systems that continuously improve with each use. Private evaluations should measure whether the model truly improves outcomes that matter to the business, rather than just looking at external benchmarks. Private reinforcement learning environments should enable the model to become stronger based on real trajectories from within the organization. An enterprise knowledge base would make institutional memory searchable and enhance token utilization efficiency.

This loop will become the new intellectual property of businesses. I see it as a "climbing machine." Moreover, unlike most assets, it will compound. Each improvement in workflow will generate better training signals, thereby accelerating the accumulation of the unique tacit knowledge of the business. Companies that establish this system earlier will gain an advantage that is difficult to replicate, regardless of how individual model capabilities evolve in the future.

What we do not want to see is a world where every company in every industry hands over value to a few models that consume everything in sight. If all value is ultimately captured by a few models, the political and economic structure will not tolerate such an outcome. An AI future that hollow out entire industries cannot gain societal permission.

Think about what happened in the first phase of globalization: entire industrial economies were hollowed out through outsourcing. On the surface, GDP figures looked decent, but genuine industrial displacement and employment shocks did exist, and the consequences are still being felt today. We cannot bring that dynamic into the AI era—allowing a handful of AI systems to capture all economic returns while the entire industry's knowledge is commoditized and hollowed out beneath them.

In my opinion, our priority must be to build a cutting-edge ecosystem, not just an advanced model. Only then can value flow widely to every company, every industry, every country. In such an ecosystem, every organization can have its own learning loop, encode its institutional knowledge, and allow human capital and token capital to compound together.

This is also the platform spirit I have always resonated with: the value created on the platform should exceed the value captured by the platform itself; every company should be able to innovate continuously and create its own value.

When this is realized, businesses will create value for themselves and for the economic environment in which they operate. Employees' professional abilities will be amplified, their judgment will become part of the system, replicable, and scalable, and these benefits will flow back to the company and its surrounding community.

This is how businesses create value for themselves and the broader economy. It is also the stable equilibrium we should work together to build.