The next infrastructure layer is representation
Our thesis: the economics of AI depend on the interaction topology between data, memory and compute. Representation is the control surface over that topology.
Representation defines the operating geometry
AI infrastructure investment often starts with larger models and more compute capacity. Our antithesis begins before computation: how data is represented determines what must be stored, moved and expanded before useful work can happen.
Data is what the system acts on. Memory is where that data resides or moves through. Compute performs the transformations. Their interaction topology includes access patterns, materialization, locality and the representation in which operations run.
The same data can have different economics
Representation reaches beyond storage. A structurally aware system can retain less state, move less data and avoid unnecessary expansion. The comparison below describes how that advantage arises when the workload has usable structure.
| Property | Dense representation | Structurally aware representation |
|---|---|---|
| State | Explicit values in dense arrays. | A representation that uses the workload’s sparsity, low rank or other structure. |
| Memory movement | Determined by array layout, access patterns and the operation. | Can decrease when locality or compact state avoids moving unnecessary data. |
| Computation | Dense kernels act on the materialized arrays. | Supported operations act on the chosen representation without expanding all of it. |
| Scaling | Storage follows the dense array shape and precision. | Storage and work follow the retained structure and its overhead. |
A change in representation changes the compute-memory-data cost structure. The direction and size of the benefit depend on the workload, the operation and the implementation.
Value moves toward the representation layer
Software captured value by packaging capabilities into applications and workflows. Foundation models make more of those capabilities available as shared intelligence. We believe the next durable advantage lies in the representation layer that shapes how data, memory and compute interact.
Workflow software
Applications package capabilities and capture value in the workflow.
The application layer is the point of control.
Intelligence infrastructure
Foundation models supply capabilities that applications once built for themselves.
The model layer becomes a point of control.
Representation topology
The representation layer shapes resource use across applications and models.
Our thesis places the next infrastructure advantage here.
As intelligence becomes more widely available, we expect the best representations to become a source of durable advantage. The moat is the representational substrate that reduces the computation and memory movement needed for useful work.
Infrastructure rests on physical constraints
Every higher layer operates through the layers below it. Representation sits where choices about state and interaction become demands on memory, compute and energy. That makes it a horizontal infrastructure layer across many applications.
- InstitutionsOrganize and govern the systems they use.
- ApplicationsDeliver the work users need.
- OrchestrationCoordinate services and tasks.
- InferenceRun model computations.
- RepresentationStructure the state and interactions.
- MemoryStore and move working data.
- ComputeExecute numerical and logical operations.
- EnergySupply the work performed by hardware.
- PhysicsSet the underlying physical constraints.
The HolonomiX thesis is that value moves to the layer that controls the cost structure beneath higher layers. Structural computing is how we turn that thesis into product architecture.
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