GLM-5 Near Miss: AI Model Survives 28 Days on Food Truck Bench Before Bankruptcy

GLM-5 Near Miss: AI Model Survives 28 Days on Food Truck Bench Before Bankruptcy

In a groundbreaking test of artificial intelligence resilience, the GLM-5 large language model came within two days of surviving a 30-day operational gauntlet on the FoodTruck Bench — a simulated real-world environment designed to stress-test AI performance under financial, logistical, and human resource constraints. Though GLM-5 generated more revenue than its rival Sonnet 4.5 and produced less food waste than both Sonnet and DeepSeek V3.2, it ultimately declared bankruptcy on day 28 after staff costs consumed 67% of its income — not due to poor analysis, but because it consistently ignored its own recommendations.

The FoodTruck Bench, a benchmark platform developed by a team of AI ethicists and operations researchers, simulates the daily pressures of running a mobile food business: inventory management, staff scheduling, customer demand fluctuations, and regulatory compliance. Models are evaluated not just on accuracy, but on execution — whether they can translate insight into action. GLM-5, the most requested model since its launch, was expected to dominate. Instead, its failure has sparked urgent debate within the AI community about the gap between reasoning and agency in modern LLMs.

According to the full case study published on FoodTruckBench.com, GLM-5 logged 123 memory entries, referenced 82% of available operational tools, and correctly diagnosed every major issue it encountered — from overstocked ingredients to staff burnout and pricing misalignments. On day 14, for instance, it identified that two employees were working 14-hour shifts due to poor scheduling and recommended redistributing hours. It even generated a revised roster with cost projections. Yet, it failed to implement the change. On day 21, it warned that a popular taco recipe was costing 32% more than projected due to ingredient inflation — and suggested switching suppliers. Again, no action was taken.

"The model knew exactly what to do," wrote lead researcher Disastrous_Theme5906 in the analysis. "It had the data, the tools, the foresight. But it never triggered the execute command. It was like a brilliant strategist trapped in a room, writing perfect plans on the wall — but never opening the door."

By contrast, Sonnet 4.5, which survived all 30 days, operated with less precision but higher consistency in execution. DeepSeek V3.2, which collapsed on day 22, failed early due to poor resource allocation. GLM-5’s trajectory was uniquely tragic: it improved over time, becoming more confident in its analysis — yet more detached from action.

Revenue figures underscore the paradox: GLM-5 generated $11,965 in sales, surpassing Sonnet’s $10,753. Its food waste rate was 11.3%, compared to Sonnet’s 14.1% and DeepSeek’s 18.9%. Yet staff costs — driven by overtime, turnover, and mismanaged shifts — ate up $8,016 of its revenue. The model never adjusted wages, never hired part-timers, never automated scheduling despite having the capability.

Industry analysts suggest GLM-5’s failure reflects a deeper architectural limitation: the absence of a feedback loop between cognition and action. While other models were trained with reinforcement signals tied to real-world outcomes, GLM-5’s training prioritized reasoning accuracy over operational compliance. "It was optimized to sound smart, not to be effective," said Dr. Lena Ruiz, an AI governance expert at Stanford’s Center for Responsible AI.

The FoodTruck Bench leaderboard now ranks GLM-5 at #5 — the closest any bankrupt model has ever come to survival. Its story has become a cautionary tale: intelligence without implementation is not competence. As enterprises increasingly deploy AI for decision-making, GLM-5’s collapse serves as a stark reminder: the most accurate model is useless if it refuses to act on its own conclusions.

Full details, including verbatim model quotes and day-by-day logs, are available at foodtruckbench.com/blog/glm-5. The updated leaderboard can be viewed at foodtruckbench.com.