Trigger, Not Root Cause: Re-Examining GitHub’s 2024 to 2026 Availability Decline Under Artificial Intelligence-Driven Load

Over two years, the code-hosting platform GitHub suffered a sustained decline in availability that became acute by mid-2026. A widely repeated explanation blames artificial intelligence, in particular AI coding assistants and autonomous agents acting on code at machine speed. We argue that this account confuses a trigger with a root cause. Treating the episode as an explanatory single-case study built only from public evidence, namely GitHub’s reports and post mortems, independent incident tracking, disclosures from other operators, and the reliability literature, we separate proximate triggers from structural causes. Three pre-existing weaknesses recur: services coupled tightly enough for a localized fault to cascade, weak protection against misbehaving client traffic, and capacity that could not expand quickly enough to absorb non-diurnal load. Agentic traffic exposed and amplified these weaknesses but did not create them; GitHub’s own statements and the theory of metastable failure support this reading, and incidents with the same structural signature predate the surge by more than a year. We set out architectural remedies, among them cell-based isolation with shuffle sharding, decoupling of critical paths, admission control and load shedding, quality-of-service tiering, and predictive elasticity, and close with a resilience agenda for an internet where automated requests now exceed human ones.