In computing, a cache is a store of results we don’t want to recompute from scratch. It saves time, energy, and resources by remembering what we’ve already worked out.
Knowledge works the same way. Every insight we hold, every model we train, every query we’ve already run is a preserved computation. When I say, “Knowledge is Cache,” I mean that storing and reusing knowledge is not just convenient — it is the very way intelligence conserves its scarce resources.
What Knowledge Saves
If knowledge is cache, then what exactly does it save? In computing, a cache preserves more than time — it reduces the load on systems, avoids wasted cycles, and makes the whole architecture more resilient. Knowledge does the same.
- Concurrency: Serving from cache allows many requests to be satisfied at once. Without it, each new request would demand new compute. For humans, this is the difference between answering ten questions from memory in a hallway versus running ten fresh experiments.
- Electricity: Compute is physical. Every warehouse query, every GPU call, consumes watts. Caching knowledge reduces redundant cycles and lowers energy consumption. Multiply that across millions of requests, and the planetary savings are real.
- Compute Cycles: CPUs and GPUs are not infinite. Preserving results frees them for novel, harder problems. Knowledge is how we don’t reinvent the wheel at every turn.
- Network Bandwidth: Moving data is expensive — in latency, cloud egress fees, and congestion. A cache close to the query saves pulling gigabytes over the wire again. Human analogy: don’t request the same massive report from headquarters when you already have the summary on your desk.
- Other Resources: Storage, attention, patience. Every cache hit spares not only the machine, but also the human using it.
The Art of Staleness
Caching is not perfect. The real trick is knowing when to trust what’s stored and when to refresh:
- Some things change rarely, so a preserved record remains useful.
- Some things change constantly, making it cheaper to recompute—like the saccades of our eyes, refreshing vision many times per second.
- Most things are in-between, where we weigh the risk of being wrong against the cost of recomputation.
This balancing act is not only a system design problem—it is the core of judgment, whether in AI or in life.
Knowledge as Living Cache
The more we can reliably and inexpensively detect staleness, the more effectively knowledge functions as cache. Done well, it enables concurrency, conserves electricity, reduces network strain, and focuses compute on what is genuinely new.
That’s why knowledge is not only power. Knowledge is cache. It preserves the cost of prior compute so we can push forward without wasting what we’ve already earned.
Soft Coded Logic 