AI and the End of Friction-Based Business Models

Artificial intelligence is not just improving how work gets done. It is removing the complexity many business models exist to manage.

TL;DR: Much of the modern economy exists to help humans navigate complexity. Artificial intelligence changes this by scaling the ability to interpret, decide, and act without human limitation. As friction disappears, business models built on managing complexity may gradually lose their structural foundation, not because they failed, but because they are no longer necessary.

For most of modern economic history, friction has been an invisible but essential ingredient of value.

Friction slowed decisions. It introduced uncertainty. It forced individuals and organizations to seek expertise in order to navigate complexity. Entire professions emerged to interpret rules, reconcile ambiguity, and reduce risk. Lawyers translated legal systems into actionable advice. Consultants helped organizations make sense of uncertain futures. Financial intermediaries connected buyers and sellers who could not easily find or trust one another. Software companies built tools to help humans manage processes that were too complex to handle manually.

These roles were not accidents. They were structural responses to the limits of human cognition and coordination.

The modern economy, in many respects, is built on top of those limits.

Every time a system becomes difficult to navigate, an opportunity emerges for someone else to simplify it. That simplification becomes a service. That service becomes a business. Over time, entire industries form around helping humans overcome the inherent friction of operating in complex environments.

This pattern is so pervasive that it often goes unnoticed. Most organizations do not think of themselves as friction managers. They think of themselves as solution providers, advisors, or platforms. But beneath those identities lies a more fundamental function. They exist because the underlying system is too difficult, too opaque, or too slow for individuals to navigate alone.

Artificial intelligence changes this equation in a way that is easy to underestimate.

For the first time, it becomes possible to remove friction not by simplifying the system, but by scaling the ability to navigate the system itself. Instead of reducing complexity, AI absorbs it. Instead of making processes easier for humans to perform, it performs them directly.

This is a subtle but profound shift.

Historically, technology has often reduced friction by standardizing workflows or improving access to information. Spreadsheets made financial modeling easier. Search engines made information easier to find. Cloud software made infrastructure easier to provision. These innovations improved human capability, but they still depended on human interpretation and action.

Artificial intelligence operates differently. It does not merely present information. It interprets it. It does not merely accelerate workflows. It executes them. It does not merely support decision-making. It participates in it.

As a result, many forms of friction that once justified entire layers of economic activity begin to disappear.

Consider how much of modern enterprise software exists to help humans manage procedural complexity. Systems of record track transactions. Systems of engagement facilitate communication. Systems of analysis generate insight. Each layer exists because humans require structured support to operate effectively at scale.

When AI systems can navigate those layers directly, their purpose begins to change. The value no longer resides in helping humans operate the system. The value resides in the system’s ability to operate itself.

This does not mean these systems disappear immediately. Institutions rarely vanish overnight. They persist, often long after their original function begins to erode. But their structural foundation weakens. Their role shifts from essential infrastructure to transitional scaffolding.

The same dynamic applies beyond software.

Entire categories of professional expertise exist to bridge the gap between complexity and human limitation. Financial advisors help individuals interpret markets. Consultants help organizations interpret uncertainty. Analysts help leaders interpret signals buried in data. These roles exist because interpretation itself has historically been scarce and expensive.

Artificial intelligence introduces interpretation at scale.

When interpretation becomes abundant, the economic logic supporting many intermediary roles begins to change. The value is no longer defined solely by access to expertise. It is defined by how that expertise is integrated into decision-making systems that operate continuously.

This creates a subtle but significant form of structural risk.

Recent analysis from both Dario Amodei and Citrini Research helps clarify why this shift feels different from previous technological transitions. Amodei frames artificial intelligence as the emergence of scalable cognition itself, capable of operating continuously and at speeds that exceed human organizational response. Citrini, approaching the question from an economic perspective, describes how systems built around human cognitive participation may become unstable as that participation becomes less necessary. Taken together, these perspectives point toward the same underlying reality. Artificial intelligence is not merely improving how existing institutions function. It is changing the conditions that made many of those institutions necessary in the first place.

Organizations built on managing friction may discover that friction itself was the foundation of their value. As friction diminishes, the justification for their existence becomes less clear. They are not disrupted because they failed to execute. They are disrupted because the conditions that made their function necessary no longer apply.

This is not a failure of leadership or strategy. It is a shift in the environment.

Every technological transition alters the economic landscape to some degree. New capabilities emerge. Old capabilities lose relevance. What makes artificial intelligence different is the layer at which it operates. It does not simply improve execution within existing structures. It changes the relationship between intelligence and complexity.

When intelligence can be applied at scale, complexity itself becomes less of a barrier.

And when complexity ceases to function as a barrier, many of the structures built to manage it begin to lose their structural footing.

From the outside, this process may appear gradual. Organizations continue to operate. Services continue to be delivered. Revenue continues to flow. But beneath the surface, the underlying logic begins to shift. What once functioned as a durable source of value becomes increasingly contingent.

The risk is not immediate collapse. The risk is gradual erosion.

Business models built on friction rarely announce their own obsolescence. They persist until they are quietly replaced by systems that no longer require what they provide.

From the inside, the change often feels incremental.

From the outside, it eventually appears inevitable.