2507 Head-to-Head Tests Reveal Stable 23% Failure Rate for AI Versus Traditional Statistics Through 2025

The Manso et al. corpus aggregates results from peer-reviewed papers published 2000-early 2025 that directly pitted AI models against statistical baselines or numerical solvers on identical tasks. Performance deltas were extracted alongside reported FLOPs or wall-clock time, producing paired outcome-cost records. In statistics comparisons, median accuracy gains reached 4-11 percentage points but median compute multipliers exceeded 30x. Against scientific computing codes, AI showed median accuracy deficits of 2-7 points until 2020, after which the sign flipped to a 3-point median advantage while retaining a 5-12x cost reduction.

These patterns align with documented behavior in protein structure prediction and turbulence modeling, where learned surrogates accelerate inference yet fail to match the convergence guarantees of established discretizations on out-of-distribution inputs. The persistent 23% quadrant of higher-cost, lower-performance AI deployments indicates systematic mismatches between model inductive biases and problem structure rather than transient data or hardware effects. Regulatory filings on high-performance computing allocations further show that labs continue provisioning both AI accelerators and traditional solver nodes in fixed ratios, consistent with the measured trade-off surface.

Operational uptake therefore favors hybrid pipelines that route routine inference to cheaper AI modules while retaining verified solvers for validation or extrapolation regimes. Continued gains post-2020 track improvements in physics-informed architectures and larger training corpora, yet the unchanged failure rate against statistics suggests fundamental limits in low-data or high-precision settings will persist without new theoretical constraints.