Probabilistic forecasters are increasingly learned, yet the baselines they are compared against are often weak or omitted. We show that the simplest possible conformal interval — a last-value point forecast wrapped in a finite-sample split-conformal residual quantile, with no parameters and no training — is a far stronger baseline than its near-total absence from recent learned-forecasting and conformal–time-series comparisons would suggest. In one-step-ahead online forecasting across 2,217 real series spanning nine public sources (the Monash archive, the LOTSA collection, the LTSF traffic/electricity/weather suites, METR-LA, BOOM, and nips/probts), this ConformalNaive interval decisively beats the naive value-quantile baselines, the entire NPTS family (NPTS 73%, SeasonalNPTS 64% of series), and the published Conformal Seasonal Pools (CSP) method (71% of series, bootstrap 95% CI [69, 73], p ≈ 7.6 °ø 10−135); it is on par with the simpler learned conformal predictors (RCI, quantile regression; median relative Winkler within 2%) and is beaten only by the adaptive-online and ensemble conformal methods (SPCI, ACI, AgACI), which explicitly track distribution shift and lead by 9–33% relative Winkler. It is also better calibrated than a trained neural forecaster: on the six datasets that introduced DeepNPTS, the trivial conformal floors cover the truth 84–85% of the time at a nominal 95%, versus DeepNPTS’s 66%. At multi-step seasonal horizons the picture inverts: the random-walk floor is the weakest method and the seasonal pool (CSP) wins — a boundary we map so practitioners know when complexity is actually required. Finally we give ConformalNaive+, a one-line, training-free, horizon-adaptive selector that attains the better of two complementary floors at every horizon with restored coverage. We argue the matching conformal naive floor must be a mandatory baseline whenever a learned probabilistic forecaster claims gains.