Xubin Luo

Xubin Luo

A decentralized ecosystem can capture value and still fail to fund the actors who keep it running. Users may pay fees, tokens may appreciate, issuers may earn revenue, and protocols may burn value, but none of these facts by itself shows that authors, miners, validators, suppliers, storage providers, or other critical participants are actually compensated. This paper argues that traditional value-capture analysis often assumes a centralized pool: once value is captured, it can be reallocated through budgets, contracts, payroll, or managerial discretion. Decentralized ecosystems do not have this default pool. They require routed closure: captured value must pass through a verifiable route to a specified critical incentive recipient, and it must be sufficient relative to that recipient's reward requirement. We formalize this distinction through Route-Admissible Value and operationalize it with the External Value Routing Closure protocol. A contrast set including YouTube, Steem/Steemit, Bitcoin, Ethereum, Aave, Filecoin, USDC, and XRP shows why revenue, fees, burns, token prices, or market capitalization should not be mistaken for sustainable incentive funding.

Xubin Luo, Yang Cheng

AI inference is becoming a persistent and geographically distributed source of electricity demand. Unlike many traditional electrical loads, inference workloads can sometimes be executed away from the user-facing service location, provided that latency, state locality, capacity, and regulatory constraints remain acceptable. This paper studies when such digital relocation of computation can be interpreted as latency-constrained relocation of electricity demand. We develop an energy-geography framework for geo-distributed AI inference. The framework models a three-layer architecture of clients, service nodes, and compute nodes, and formulates inference placement as a constrained optimization problem over electricity prices, marginal carbon intensity, power usage effectiveness, compute capacity, network latency, and migration frictions. The key object is the energy-latency frontier: the marginal cost and carbon benefit unlocked by relaxing inference latency budgets. The paper makes four contributions. First, it distinguishes physical electricity transmission from digital relocation of electricity-consuming computation. Second, it formulates a geo-distributed inference placement model with feasibility masks and migration frictions. Third, it introduces operational metrics, including relocatable inference demand, energy return on latency, carbon return on latency, and a relocation break-even condition. Fourth, it provides a transparent stylized simulation over representative global compute regions to show how heterogeneous latency tolerance separates workloads into local, regional, and energy-oriented execution layers. The results show that latency relaxation expands feasible geography, while migration frictions, egress costs, state locality, legal constraints, and capacity limits can sharply reduce realized benefits.