alt.hn

2/20/2026 at 11:02:36 AM

Ask HN: How to measure how much data one can effectively process or understand?

by mbuda

2/20/2026 at 4:25:58 PM

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.

by allinonetools_

2/20/2026 at 3:46:57 PM

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.

by mikewarot

2/20/2026 at 1:45:16 PM

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.

by kellkell

2/20/2026 at 2:09:06 PM

Yep, amazing points!

Agree with the measures; follow-up question: what's the insight definition? I think exposing some of those measures would help people better understand what the analysis covered, in other words, how much data was actually analyzed. Maybe an additional measure is some kind of breadth (I guess it could be derived from the throughput).

"Informational leverage" reminded me of "retrieval leverage" because yeah, the scale of data didn't change, the ability to extract insights did :D

by mbuda

2/20/2026 at 7:06:33 PM

Good question.

By “insight” I mean a measurable reduction in uncertainty that improves decision quality or predictive accuracy.

In practical terms, an insight could be defined as:

•A hypothesis generated and testable from the dataset

•A model parameter adjustment that increases predictive performance

•A structural relationship discovered that reduces entropy in the system representation

So compression efficiency would be something like:

(uncertainty reduced) / (data processed)

Breadth is interesting — I’d treat it as dimensional coverage: how many independent variables or graph regions are meaningfully integrated into the model.

“Retrieval leverage” is a great term. It highlights that the dataset size remains constant, but navigability and relational traversal improve — which increases effective cognitive reach.

Some of these broader ideas around informational sovereignty and anomaly-driven cognition have been explored in independent empirical work, though they’re still niche.

by kellkell

2/20/2026 at 2:53:52 PM

lol comment, ignored.

by Natfan

2/20/2026 at 6:13:16 PM

I would measure data by time to action. If you're not actioning data it's worthless.

by rgavuliak