The problem of impaired individuals making poor food delivery decisions—such as ordering late at night while drunk and then failing to receive their delivery—creates inefficiencies for users, drivers, restaurants, and platforms. Wasted food, lost earnings for drivers, and regretful spending by users suggest a clear need for intervention.

A Smarter Approach to Delivery Decisions

One way to address this issue could be through a system that detects potential user impairment using behavioral signals. For instance, the app might flag unusual ordering patterns like erratic typing, excessive item quantities, or deviations from a user's typical habits. Late-night orders could automatically trigger additional safeguards, such as requiring confirmation steps or suggesting pre-made meals that are less likely to go to waste. Drivers could also be notified of higher-risk deliveries, allowing them to adjust accordingly. This wouldn’t prevent orders but would nudge users toward more reliable decisions.

Stakeholder Benefits and Implementation

This approach could benefit all parties involved: users avoid wasted money, drivers save time on unclaimed orders, and platforms reduce operational inefficiencies. A simple MVP might focus on time-based flags (e.g., extra confirmations after midnight) and basic pattern recognition, tested with a small delivery partner. Over time, refining the detection system with real-world data could improve accuracy while minimizing false positives. Collaborating with existing platforms, rather than competing directly, might be a viable path to adoption.

While no major delivery service currently addresses this issue, a system that reduces waste and improves experiences could differentiate itself. The key would be balancing user convenience with effective safeguards, ensuring the solution is practical rather than intrusive.