Fir example, a correct grand unified theory isn't useful if you don't know the physics to understand it.

Humans don’t process data directly — we process compressed representations. So a meaningful scale would measure:

1- Throughput — how much structured data an agent can analyze per unit time.

2- Compression efficiency — how much insight is extracted per unit of data.

3- Relational depth — how many meaningful relationships can be modeled simultaneously.

Tools like Agentic Runtimes + GraphRAG don’t just increase data volume access — they expand relational modeling capacity and contextual memory. In that sense, they move users up a scale of informational leverage, not just scale of data.