Building an operating system layer for AI agents appears slower than building a specific AI application. The OS approach ships fewer features, demonstrates less immediate value, and confuses users expecting point solutions. History suggests this is exactly correct.
The Database Precedent
Early databases seemed like overkill. Why use Oracle when you could just write file handling code? Custom solutions were faster to build for specific needs.
But databases provided durability, consistency, query optimization, and concurrent access that every application needed. Building these capabilities into each application was eventually recognized as absurd duplication.
The database became infrastructure. Applications became consumers of that infrastructure. The total system became more capable than any single application could have been.
Databases looked slow to start. They became the foundation of enterprise software.
The Browser Precedent
Early web browsers were document viewers. Less capable than native applications. Slower. Uglier. Limited functionality.
But browsers provided universal reach, automatic updates, and platform independence. Applications built for browsers could run anywhere.
Over time, browsers became application platforms. The "limitations" became features — sandboxing, permissions, standard APIs.
Chrome started as a simple browser. It became the dominant application platform for enterprise software.
The Kubernetes Precedent
Kubernetes was notoriously complex. Just running a simple application required understanding pods, services, deployments, and ingress. Critics asked: why not just use VMs?
But Kubernetes provided orchestration, scaling, and operational consistency that every production system needed. The complexity was front-loaded, but it paid dividends at scale.
Today, Kubernetes is default infrastructure. The question isn't whether to use it but how to use it effectively.
Kubernetes looked like unnecessary complexity. It became the foundation of cloud-native operations.
Why Infrastructure Wins
Infrastructure succeeds when it captures capabilities that every application needs. Security, durability, consistency, integration — these requirements are universal.
Building these capabilities into applications duplicates effort and introduces inconsistency. Building them into infrastructure amortizes the investment across all applications.
The infrastructure provider who gets these capabilities right becomes the foundation everything else builds on. That's a defensible position.
An Agent OS that provides execution control, privacy, and reliability for any AI application is infrastructure. Individual applications that each implement these capabilities are duplicating effort that will eventually consolidate.
Key Takeaway
Infrastructure appears to move slowly because it solves general problems rather than specific ones. But general solutions compound in value. An Agent OS approach is slower to demonstrate but creates a more defensible and valuable foundation than point applications.