Delphi

Delphi · synthetic populations as a computational primitive. You ask any question — Will the Fed cut rates in Q3? Pretest this tagline. Stress-test this decision — and a swarm of Gemini 3.5 Flash sub-agents, each role-playing a different American persona generated from real US Census demographic axes and grounded in live web, reasons in parallel. In ~60 seconds you get back: a probabilistic forecast with confidence interval, the strongest reasons for and against drawn from agents' own reasoning, the demographic axes where groups diverged most, and a striking outlier quote — all synthesized into a Wall Street Journal–style summary by a final Gemini call. Live-validated on stage at N=500 with a +66.2 percentage point shock response (13.3% → 79.5% when CPI surprised). Validation across 27 automated tests, an adversarial persona-stability harness (4.92/5 in-character, 0% drift to centrist mean), and a cross-model comparison that showed Gemini 3.5 Flash holds 100% per-agent success where Gemini 2.5 Flash collapses to 12.5% on identical prompts. Until Gemini 3.5 Flash made hundreds of parallel grounded reasoning agents economical at conversational latency, this category did not exist. Forecasting is the wedge — the same primitive powers marketing pretests, policy war-gaming, synthetic juries, and behavioral pre-mortems.