The ShakeAlert® earthquake early warning (EEW) system is designed to warn users of imminent strong ground motion with sufficient time to take protective actions. ShakeAlert currently uses three algorithms to characterize the earthquake source. One estimates the location and magnitude using the first few seconds of the P wave and, while fast, tends to underestimate magnitude for M_w 7.0+ earthquakes. A second estimates the location, orientation, length, and corresponding magnitude of a line source using observed peak ground acceleration and contributes primarily to M_w 5.5+ earthquakes. The third infers earthquake magnitude from peak ground displacement measured using Global Navigation Satellite System (GNSS) data and offers nonsaturating magnitudes for  M_w7.0+ earthquakes. Other EEW algorithms exist that infer temporally evolving spatially variable slip on a 3D fault surface from real‐time GNSS data, information that might enable more accurate and timely alerts in the event of large‐magnitude subduction interface earthquakes. Here, we evaluate the potential contribution of one such algorithm, BEFORES (Minson et al., 2014), to improve ShakeAlert performance through a simulated real‐time implementation of Bayesian evidence‐based fault orientation and real‐time earthquake slip (BEFORES) and other ShakeAlert algorithms using data for eight M_w 7.6+ earthquakes. The test results demonstrate that BEFORES can produce well‐constrained and accurate magnitude estimates as soon as or sooner than other EEW algorithms, in turn enabling it to increase the amount of warning time users receive in many cases. However, with a modified Mercalli intensity (MMI) threshold of 3.5, which is commonly used for issuing alerts, BEFORES would tend to alert large geographic regions that did not feel strong shaking (MMI 6+). This effect can be mitigated using a higher alert threshold of MMI 4.5 without negative impact on the amount of warning time obtainable with BEFORES.