Speculative Execution in AI Pipelines: Cutting Latency by Betting on the Future
Most LLM pipelines are embarrassingly sequential by accident. An agent calls a weather API, waits 300ms, calls a calendar API, waits another 300ms, calls a traffic API, waits again — then finally synthesizes an answer. That 900ms of total latency could have been 300ms if those three calls had run in parallel. Nobody designed the system to be sequential; it just fell out naturally from writing async calls one after another.
Speculative execution is the umbrella term for a family of techniques that cut perceived latency by doing work before you know you need it — running parallel hypotheses, pre-fetching likely next steps, and generating multiple candidate outputs simultaneously. These techniques borrow directly from CPU design, where processors have speculatively executed future instructions since the 1990s. Applied to AI pipelines, the same instinct — commit to likely outcomes, cancel the losers, accept the occasional waste — can produce dramatic speedups. But the coordination overhead can also swallow the gains whole if you're not careful about when to apply them.
