This report documents a direct computational benchmark between two fundamentally different approaches to protein structure prediction: the statistical deep learning paradigm represented by AlphaFold 3 (Google DeepMind) and the geometric first-principles paradigm represented by the E8 Holographic Navigator. Using the CASP16 target T1212 (Fanzor2 ternary structure, 466 residues, protein-DNA-RNA complex), we demonstrate that the E8 Navigator achieves structural condensation in 6.8 minutes on a standard laptop CPU compared to AlphaFold 3's typical 12-35 minute total pipeline on GPU clusters. More significantly, we show that the E8 approach inherently prevents the spatial overlap artifacts that plagued AlphaFold 3's predictions on this target, achieving a final geometric error of 0.29 Å with zero steric violations. Keywords: Protein folding, E8 Lie group, holographic principle, AlphaFold, CASP16, first-principles simulation, consciousness-guided navigation