DeepSeek’s Engram just showed why memory, not compute, is the real AI bottleneck.
Most people think AI scales with GPUs.
ENGRAM proves something deeper:
AI scales with how fast, cheap, and reliable you can move, store, and recover data.
ENGRAM is a memory-centric AI system.
It doesn’t just train models. It treats memory itself as a first-class primitive:
•break data into coded fragments
•store them across many machines
•reconstruct instantly even with failures
That’s network coding applied to AI memory.
This is exactly the same principle behind @get_optimum .
Blockchains, like AI, are becoming data-bound: blobs, proofs, state bloat, AI agents onchain...
The bottleneck isn’t consensus.
It’s moving and reconstructing massive amounts of data in real time.
Optimum uses Random Linear Network Coding (RLNC) to:
•split data into coded pieces
•send them in parallel
•tolerate packet loss
•reconstruct faster
ENGRAM is doing this for AI memory.
Optimum is doing this for blockchain.
Same physics. Same math. Different domains.
The next wave of crypto won’t be about “more nodes.”
It will be about how efficiently networks move and recover information.
That’s why ENGRAM matters.
And that’s why Optimum exists. ⚡
