Interesting question. In practice, I’ve found the limit isn’t how much data exists but how much you can turn into action without friction. The clearer and faster the feedback loop, the more data you can effectively “use,” regardless of volume.

The limiting factor would be the density of information in the source material, followed my the cognitive impedance match of the receiver.

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

The Kardashev scale measures energy control, not information processing. If we were to define a “Kardashev scale for data,” it wouldn’t be about raw volume, but about effective abstraction capacity.

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.

Here are clickable links: https://youtu.be/ygr8yvIouZk?t=1307, https://adamdrake.com/from-enterprise-decentralization-to-to...