The problems arise because we envision the programs’ State as a large, consistent data-centric model. If you must maintain a single, globally consistent state,

The problems arise because we envision the programs’ State as a large, consistent data-centric model. If you must maintain a single, globally consistent state, you must implement global blocking primitives, which are bound by latency and correctness requirements on all parties in the transaction. The lower bound for cost on these types of global state engines in a Byzantine environment seems to have been found by eg. Hashgraph: a few tens of thousands of distributed transactions per second in a globally distributed environment. Falls apart for multi-planetary (high latency) participants… Reframing the global State as “agent-centric” — only participating agents must establish consistency (instead of data-centric - all participants must agree upon one globally consistent state) removes this limit; it appears that we can scale overall transaction rate linearly with node count, roughly. This is the big breakthrough coming with eg. Holochain based systems. It takes some time to reframe algorithms in an agent-centric form…