Allen Institute for AI (Ai2) has replaced its priority-based GPU scheduling system with a new time-budget allocation model designed to fairly distribute compute resources among 150+ researchers. The change addresses persistent conflicts over access to thousands of NVIDIA H100, B200, and B300 GPUs, where demand exceeds available capacity by 2-3x. The old priority system suffered from what Ai2 calls "tragedy of the commons" dynamics: researchers would park no-op workloads on GPUs to prevent shutdown delays, priority levels became uniformly inflated as teams requested HIGH status en masse, and infrastructure engineers spent much of their time negotiating the termination of protected workloads during maintenance. Ai2's infrastructure team, drawing on economics research into fair resource allocation, realized that fixed GPU monopolies caused idle capacity during seasonal research fluctuations while priority negotiations created operational friction. The new system allocates GPU time (rather than physical resources) to research teams based on strategic priorities set at budget time, before workloads are submitted. This approach combines budgets, hierarchical fair-share allocation, and time-slicing contracts to maintain full cluster utilization while shifting resource contention from operational negotiation to transparent administrative budgeting.