When AI Advisors Agree: Distinguishing Forecast Risk from Forecast Reliability
If seven AI advisors agree, what have we learned: that their forecast is reliable, or that the asset is easier to forecast? We examine 928 completed first-quarter outcomes for 232 assets across four historical iPulse AI batches. Ten Gemini configurations form seven equally weighted investment frameworks; no forecasts are regenerated. The council has mean absolute return error of 15.15 percentage points (pp), versus 14.99 for no change, with an unresolved paired difference of +0.16 pp. Error rises from 10.31 to 21.05 pp across disagreement quartiles, but the no-change baseline rises almost identically, from 9.89 to 21.15. In the pooled archive, selecting the lowest-volatility quarter produces lower error than selecting the least-disagreeing quarter: 7.97 versus 10.31 pp. Directional agreement introduces a second trap: its apparent value depends on the aggregation rule. When at least six of seven frameworks share a direction, the sign of their mean return is correct in 53.21% of cases, versus 44.11% with lower agreement. Scoring the modal direction instead changes the lower-agreement accuracy to 54.27%, removing that positive contrast. Recomputed-rank bootstraps, scale-aware losses, and period sensitivities qualify these descriptive results; four overlapping periods cannot establish future-regime performance. Agreement, visible asset risk, and aggregation-specific skill should be evaluated separately before consensus is presented as confidence.