Preface
When working on new research or analysis, I like to apply first-principles thinking, and I believe it'll be applicable here too. My chosen writing style will aim to reflect this. Additionally, the framework developed in this article should be viewed as a hypothesis rather than a completed theory, and is intended as a conceptual model that future empirical work could validate or reject.
Main Idea
Historically, monetary systems competed for one scarce resource; Consensus: the collective expectation that others will continue accepting the same monetary instrument in future exchanges. Once accepted as the standard of exchange and as individuals accumulated monetary surpluses, they needed a way to preserve purchasing power; hence, money evolved from just being a medium of exchange into a warehouse of utilities.
A stablecoin mirrors a monetary system and hence is susceptible to capital formation. Through this lens, we understand that different players in the system may act differently when presented with different choices, and as a result, holders or circulating supply shouldn't be treated as an absolute when evaluating a stablecoin's revenue. Stablecoin protocols typically earn revenue from yields on idle capital, minting/redemption fees, transaction fees, spreads, and institutional integration. When taken into consideration, we can define revenue as a function of demand. In a coordination equilibrium, revenue is best described as a derivative of demand because it explains why a protocol remains solvent as long as users continue to hold, transact, and mint.
Migration cost & Demand correlation
If stablecoin revenue is fundamentally a function of demand, then the defensibility of a stablecoin revenue can be measured by its ability to sustain that demand. Prior frameworks try to measure this using predefined metrics such as supply and TVL; however, a more proactive approach is needed
I inferred two first-order determinants: Migration cost and Demand correlation
Migration cost in this context is the economic, operational, and behavioral friction incurred by a user when trading between stablecoins. These constraints may stem from liquidity fragmentation, poor settlement infrastructure, or regulatory constraints. An increase in the cost of migration will lower the probability of capital exiting the system in response to competing alternatives, increasing the persistence of demand and, by extension, the defensibility of protocol revenue.
Migration cost alone, however, is an incomplete measure of defensibility. A protocol may exhibit substantial user stickiness while remaining exposed to highly concentrated user demand. This introduces a second variable: demand correlation.
Demand correlation measures the extent to which users or capital pools respond to the same coordination event. Highly correlated demand implies that a large share of economic activity can migrate simultaneously because participants share a common dependency, whereas uncorrelated demand is distributed across economically independent actors whose migration decisions occur asynchronously.
The replacement of USDH within the Hyperliquid ecosystem illustrates this dynamic. Because the protocol's economic activity was overwhelmingly concentrated within a single environment, the migration decision became effectively centralized. A single governance outcome was sufficient to eliminate the primary source of demand supporting the protocol's revenue model. In retrospect, the vulnerability was not merely low migration cost but near-perfect demand correlation.
Following this framework to its conclusion, a stablecoin’s revenue moat is strengthened as the cost of switching increases and the demand correlation reduces. A protocol does not necessarily need both. High migration costs alone can produce durable demand, just as highly decentralized, uncoordinated switching behavior can make even low-friction capital remarkably persistent. The strongest moats combine both characteristics. Conversely, protocols that outsource demand acquisition to a narrow set of counterparties implicitly concentrate existential risk into a handful of coordination events.
A possible representation for the framework is:
Where:
- — Revenue defensibility
- — Aggregate migration cost
- — Demand correlation
- — Context-specific scaling factor
- — Elasticity of revenue defensibility with respect to migration cost
- — Elasticity of revenue defensibility with respect to demand correlation
Scenario analysis
To evaluate the explanatory power of the proposed framework, we apply it to three stablecoins with distinct demand architectures. Rather than comparing market capitalization or adoption alone, the analysis focuses on how migration cost and demand correlation shape the persistence of demand and, consequently, the defensibility of protocol revenue.
Case Study I: USDT
USDT represents a highly distributed demand architecture. Adoption is dispersed across exchanges, wallets, payment providers, decentralized applications, institutions, and retail users spanning multiple blockchain ecosystems. Although users continuously substitute between stablecoins in response to liquidity conditions, incentives, or market preferences, these migration decisions occur largely independently. Consequently, the departure of any individual participant or distribution channel has only a marginal impact on aggregate demand.
This distribution of demand produces two reinforcing effects. First, USDT benefits from substantial migration costs arising from deep liquidity, extensive infrastructure integration, and broad ecosystem acceptance. Second, because demand originates from numerous economically independent participants, correlation remains relatively low despite constant user turnover. The framework therefore predicts a highly defensible revenue model, where isolated migration events are insufficient to materially impair protocol earnings.
Case Study II: USDH
USDH illustrates the opposite demand architecture. Rather than emerging from a broad and independent user base, demand was overwhelmingly concentrated within a single ecosystem. As a result, economic activity became tightly coupled to one dominant coordination mechanism.
When the ecosystem elected to replace USDH as its preferred settlement asset, demand migrated almost instantaneously. The collapse was not primarily the consequence of deteriorating product quality or declining user preference, but of structural dependence on a single coordination event. Under the proposed framework, USDH exhibited comparatively low migration costs and exceptionally high demand correlation, resulting in weak revenue defensibility despite previously achieving meaningful adoption.
Together, these cases suggest that revenue durability depends less on the quantity of demand than on its underlying structure. Distributed demand produces resilient revenue because migration occurs incrementally, whereas concentrated demand remains vulnerable to synchronized displacement.
Case Study III: OpenUSD (OUSD)
OpenUSD presents a more interesting case because it challenges one of the framework's implicit assumptions. Unlike conventional stablecoins that primarily compete for individual users, OUSD seeks to acquire demand through independent distribution networks, including payment providers, financial institutions, asset managers, and fintech platforms. Each network subsequently serves its own class of end users while remaining economically aligned through a shared settlement asset.
Viewed through the current framework, OUSD appears to increase migration costs by embedding financial incentives directly into its distribution layer. Institutions that integrate OUSD not only adopt a settlement asset but also participate in its underlying economic model, increasing the cost of migration relative to conventional integrations.
However, OUSD simultaneously raises a deeper theoretical question. Demand is no longer composed solely of individual users, but of economically independent distribution nodes, each exhibiting its own internal demand dynamics. A payment network, for example, may primarily serve transactional users, while an asset manager may attract users seeking yield-bearing reserves. Although users within each network may behave in a highly correlated manner, the networks themselves may remain largely independent of one another.
This suggests that demand correlation alone may be insufficient to characterize revenue defensibility. Instead, the resilience of a stablecoin may also depend on the topology of its demand network, that is, how demand is distributed across independent economic nodes and how those nodes interact. Two protocols may exhibit similar levels of aggregate demand correlation while possessing fundamentally different network structures and, consequently, different revenue risk profiles.
Accordingly, the conceptual model proposed earlier may be viewed as a first-order approximation:
where revenue defensibility () increases with migration cost () and decreases with demand correlation ().
The OpenUSD architecture suggests a natural extension in which revenue defensibility is also influenced by network topology:
where represents the structural organization of demand across independent distribution networks. Formalizing this relationship lies beyond the scope of this article. Instead, it should be viewed as a promising direction for future theoretical and empirical research into the competitive dynamics of stablecoin ecosystems. If stablecoin businesses increasingly compete by acquiring networks rather than individual users, understanding the topology of demand may prove as important as understanding demand itself.