The Grade Point Average as a cumulative academic performance metric emerged from the same early 20th century standardization movement as letter grades. The 4.0 scale — assigning A=4, B=3, C=2, D=1, F=0 points — was not universally adopted until mid-century; some universities used 5-point or 6-point scales. The 4.0 system's dominance reflects network effects: once enough employers and graduate schools adopted it as a reference point, other institutions converged. Today the 4.0 scale is used by the vast majority of U.S. educational institutions, though the specific grade-to-point conversions (what counts as an A-, B+, etc.) vary meaningfully between institutions.

GPA as a predictor of post-graduate success is considerably weaker than its cultural prominence suggests. A 2019 Google internal study found GPA was essentially uncorrelated with job performance after the first few years of employment. Law school GPA shows moderate correlation with bar exam passage but limited correlation with long-term legal career success. Medical school GPA predicts board scores reasonably well but shows weaker association with patient outcomes or clinical performance. The strongest use case for GPA is as a signal in competitive selection contexts — it summarizes academic history efficiently when evaluating large applicant pools, not because it's a reliable predictor of job performance.

The 'grade cutoff' phenomenon — GPA thresholds like 3.0 or 3.5 used as automatic filters by employers and graduate programs — creates a discontinuity in the value of grade improvements near these thresholds. A student moving from 2.98 to 3.02 GPA gains significantly more in labor market value than one moving from 3.52 to 3.54, even though the absolute GPA improvement is identical. This means strategic grade management — carefully selecting which courses to take, when to drop a class, and how to allocate study time — can have returns far exceeding the nominal academic difference. Financial analysis of GPA improvement as an investment in future earnings suggests it's among the highest-ROI activities available to students near cutoff thresholds.

Cumulative vs. major GPA creates interesting dynamics in academic records. Many graduate programs in technical fields (engineering, computer science, economics) weight major GPA more heavily than overall GPA, recognizing that performance in directly relevant coursework is more predictive than performance in distribution requirements. A student who earned C's in humanities courses but A's in computer science may present a 3.0 cumulative GPA and a 3.8 major GPA — a profile far more compelling for a CS graduate program than the cumulative figure suggests. This is why graduate school applicants are often advised to calculate and explicitly highlight major GPA when it significantly exceeds their overall GPA.