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原文摘要

Generative AI coding tools have lowered the cost of writing trading software, but no systematic dataset records when these services fail. We introduce the AI Incident and Disruption Archive (AIDA), a multi-vendor registry of 5,868 deduplicated outage incidents across 30 providers from 2025 to 2026, with foundation-model incidents verified against primary status-page or postmortem URLs. Pairing AIDA with 5,041,034 verified maximal extractable value (MEV) transactions by 8,516 Ethereum addresses, their contract creations, and a 22-month Base Layer-2 panel of 1,099,593,191 swaps and 29,120 private executors, we establish two findings. First, entry into MEV expanded where code and public state suffice, as active arbitrage addresses quadrupled from late 2024 to late 2026 and liquidators grew by a third, while sandwich attackers, who need private order flow and builder access, declined. Execution nonetheless remained concentrated, with the top fifty addresses executing two thirds of verified trades. Second, AI outages stall new trading code while deployed bots keep trading. On Ethereum, addresses releasing a private executor fall to 0.67 on the day of an eight-hour disruption (0.68 for executor creations during OpenAI outage hours) and to 0.66 at lag two before rebounding on day three; on Base, operator top-level creations fall to 0.76 during OpenAI hours and executor-deploying operators to 0.88 at lag two. Non-trading creations, automated factory clones, and live arbitrage and sandwich execution across both chains remain unchanged. Cheaper code thus lowers the barrier to building MEV contracts, while private order flow, latency and capital still determine who wins execution.