This forecast resolves when a top-five AI lab publicly describes a production system that merges user-derived weight updates back into a shared base model. The technical and safety barriers to pooling untrusted deployment data are formidable, risking catastrophic forgetting, emergent misalignment, and backdoor vulnerabilities—for instance, data-poisoning research demonstrates that merely ~250 poisoned documents can suffice to implant a backdoor. Furthermore, the commercial trajectory strongly favors private, isolated per-tenant adapters (such as LoRA) and retrieval-based context injection to serve strict enterprise privacy demands. We estimate a 50% probability that the industry permanently standardizes on these isolated architectures, structurally precluding cross-customer weight pooling. Combined with a 20% chance of stalled progress due to regulatory crackdowns or insurmountable alignment challenges, there is only roughly a 30% probability of this shared-weight paradigm succeeding. Because this milestone is strictly gated by that minority trajectory, its cumulative probability caps at ~30%. Set against related questions, the median and upper percentiles (50th, 75th, and 90th) are projected to 'never' to properly reflect the low overall probability of the industry adopting shared-weight merging architectures. The 10th and 25th percentiles (2031 and 2036) estimate the timeline conditionally on the aggressive shared-weight branch materializing; in that scenario, pooling would likely arrive after foundational 7-day update cadences are established (which we estimate reaches the 10th percentile in 2030 ) but before major cross-user public failures occur (which we estimate reaches the 10th percentile in 2032 ).