Theory

We always use new technology like the old world until it forces us to change.

Early film did not invent cinema. It simply recorded theatre. The camera sat still, the stage remained intact, and actors performed outward as if nothing fundamental had changed. It looked new, but behaved exactly like what came before.

Only over time, through use, constraint, and experimentation, did the medium begin to assert itself. Techniques emerged that were not translations of theatre but departures from it.

At some point, someone cut into the set, lowered the camera, and pointed it upward to capture something that could never exist on a stage. That was not just a new angle. It was the beginning of a new grammar. The camera stopped documenting performance and started shaping it.

This pattern repeats more often than we admit. The printing press initially mimicked handwritten manuscripts. Early cars were designed like carriages. Computers reproduced paper-based workflows, only digitized. The first instinct is always to fit the new technology into the old paradigm because that is what is legible and safe.

That phase never lasts. Eventually the mismatch becomes too obvious. The capabilities of the technology begin to strain against the constraints of the inherited model, and the question shifts from how to use the new thing inside what already exists to what becomes possible now that this thing exists at all.

That is where the real transformation begins, and that is where AI agents sit now.

Most implementations still map agents directly onto organizational structures. An agent becomes a role: sales, support, operations. It inherits responsibilities as if it were simply a digital employee slotting into an existing org chart.

That is familiar, but it is fundamentally misaligned. Agents are not people, and they are not constrained by the same boundaries. They do not need the same wrappers around them for work to move coherently.

The better frame is closer to what Actor-Network Theory describes: actors inside a network of actions and relationships, where the outcome is produced by the path through the system rather than the position inside a hierarchy.

The efficient structure is not hierarchical but flow-based. Work becomes a sequence of transformations coordinated by dependencies, context, permissions, and state rather than handed off between predefined roles.

The system organizes around execution, around the movement and shaping of information, rather than around titles or departments.

Humans do not disappear in this model, but their position changes. They enter the system where judgment, context, or accountability are required, rather than serving as the primary scaffolding that defines how work is structured in the first place.

So the mistake is not adopting AI. It is using AI to preserve the past. If you do that, you only end up going relatively slow, faster.

The real opportunity is to design for the nature of the technology itself: a system where the path of work is primary, and both humans and agents are participants within that path.

That is the shift now underway. From org chart to execution graph. Most teams are still filming the stage.