The Price of Interoperability: Exploring Cross-Chain Bridges and Their Economic Consequences

Abstract

Modern blockchain ecosystems comprise many heterogeneous networks, creating a growing need for interoperability. Cross-chain bridges provide the core infrastructure for this interoperability by enabling verifiable state transitions that move assets and liquidity across chains. While prior work has focused mainly on bridge design and security, the system-level and economic consequences of cross-chain liquidity interoperability remain less understood. We present a large-scale empirical measurement study of cross-chain interoperability using a dataset spanning 20 blockchains and 16 major bridge protocols from 2022 to 2025. We model the multi-chain ecosystem as a time-varying weighted hypergraph and introduce two complementary metrics. Structural interoperability captures connectivity created by deployed bridge infrastructure, reflecting bridge coverage and redundancy independent of user behavior. Active interoperability captures realized cross-chain usage, measured by normalized transfer activity. This decomposition separates infrastructure capacity from actual utilization and yields several findings. The cross-chain network evolves from a sparse hub-and-spoke structure into a denser multi-hub core led by EVM-compatible chains. Bridge expansion and chain growth are uneven: some chains achieve broad structural access but limited realized usage, whereas others concentrate activity through a small set of routes. Overall, interoperability provision and interoperability use diverge substantially, showing that connectivity alone does not imply economically meaningful integration. These results provide a measurement framework for understanding how cross-chain infrastructure reshapes blockchain market structure and liquidity organization.

Figures (7)

• Figure 1: Evolution of the cross-chain corridor network with annual snapshots from 2022 to 2025. Nodes are chains colored by stack type, with purple for Layer-1, green for Layer-2, and orange for sidechains. A directed edge indicates at least one observed transfer from a source chain to a destination chain during the year. Node size is proportional to total (in + out) degree in the annual corridor graph. Edge width is proportional to $\log_{10}(1 + V_{ij}^{y})$, where $V_{ij}^{y}$ is the total bridged USD notional on corridor $i\!\to\!j$ in year $y$. Node positions are fixed across years for comparability.
• Figure 2: Weekly bridge-level activity (2022--2025). Stacked areas show the top-10 bridges ranked over the full window; remaining bridges are grouped as Other.
• Figure 3: Bridge composition of cross-ecosystem traffic between EVM and non-EVM chains. Left: shares by weekly transfer counts; right: shares by USD notional. Shares are normalized within each direction (nonEVM$\rightarrow$EVM and EVM$\rightarrow$nonEVM). We show the top-4 bridges by aggregate cross-ecosystem activity; remaining bridges are grouped into Other.
• Figure 4: Weekly endpoint share by chain (top-10 + Other). Each transfer is attributed to its endpoint chains and aggregated by week; stacked areas sum to one.
• Figure 5: Largest absolute net flows by chain pair over the study window (USD billions). Arrows indicate net exporter $\rightarrow$ importer. Dark bars use all bridges; light bars exclude official/native bridges. (*) marks corridors whose net direction flips after exclusion.