Why Lightning Payments Fail in Practice

Payments fail because channels lack inbound liquidity. Routing nodes are profit-driven, creating a market failure where liquidity is concentrated, not distributed.

• >80% of routing nodes operate at a loss, disincentivizing capital provision.
• Users must perform costly liquidity rebalancing or rely on custodians, defeating self-custody.
• The network's $200M+ public capacity is a fraction of needed private liquidity.

Payment pathfinding is a non-deterministic, trial-and-error process with no guarantees. This creates a poor UX of silent failures and unpredictable fees.

• ~40% failure rate for large payments due to fragmented liquidity.
• Source-based routing lacks global state, forcing probabilistic guesses.
• Contrast with intent-based systems (e.g., UniswapX, CowSwap) where solvers compete to fulfill a declared outcome.

Lightning's security model requires constant online vigilance to punish fraud, outsourcing trust to third-party watchtowers and creating a hidden custodial layer.

• ~1 week typical channel closure time creates a long attack window.
• Users must run a node 24/7 or delegate watchtower duty, a hidden operational cost.
• This contrasts with settlement-layer finality (Solana, Avalanche) or optimistic systems (Optimism, Arbitrum) with bounded challenge periods.

Lightning's core model of bi-directional payment channels creates a capital-intensive, fragmented network. Each channel must be funded and balanced, locking up capital that could be used elsewhere. This leads to:

• High operational overhead for nodes and service providers.
• Payment failures due to insufficient local liquidity, requiring complex multi-hop rebalancing.
• A poor user experience where sending and receiving require separate inbound/outbound liquidity management.

To abstract away the network's complexity, users flock to custodial wallets like Wallet of Satoshi or Strike. This creates a fatal contradiction:

• Reintroduces counterparty risk the base layer was designed to eliminate.
• Centralizes liquidity and control, defeating the purpose of a peer-to-peer network.
• Creates regulatory attack surfaces for the entire ecosystem, as custodians become choke points.

Finding a reliable path for a payment is a complex, unsolved optimization problem. Current implementations like LND or Core Lightning use heuristics, not guarantees. This results in:

• Unpredictable success rates, especially for larger amounts.
• No payment-of-last-resort mechanism; failed payments simply fail.
• A stark contrast to intent-based systems like UniswapX or CowSwap which abstract away routing complexity with solver networks.

Every Lightning channel must eventually settle on Bitcoin's base layer, which is congestion-prone and expensive. This creates a systemic risk:

• Forced, costly closures during mempool spikes can wipe out routing profits.
• Capital inefficiency as funds are stuck in long-term timelocks during disputes.
• A fundamental scaling limit, as the entire network's throughput is gated by Bitcoin's ~7 TPS and ~10-minute block times.

Managing channels, watching for fraud, and handling backups is a non-starter for normies. The required mental model includes:

• Channel states and the risk of publishing old states.
• Inbound vs. outbound liquidity as separate concepts.
• Static channel backups that must be meticulously preserved. Compare this to the one-click experience of Venmo or even Ethereum L2s like Base.

Lightning is a monolithic protocol built for a single asset (BTC) on a single chain. It lacks the modularity and programmability of modern stacks. This prevents:

• Native cross-chain swaps without wrapped assets (see THORChain).
• Complex conditional payments or intent-based transaction flows (see Anoma).
• Rapid protocol upgrades, as changes require near-universal consensus among node implementations.