AI Food Truck Experiment: Only 4 of 12 LLMs Survived 30-Day Business Challenge
In a groundbreaking simulation, 12 large language models were tasked with running a food truck for 30 days using $2,000 in startup capital. Only four generated sustainable profits, while all models that took loans failed — revealing critical flaws in AI decision-making under real-world constraints.
AI Food Truck Experiment: Only 4 of 12 LLMs Survived 30-Day Business Challenge
A novel experiment in artificial intelligence decision-making has revealed stark limitations in how large language models (LLMs) handle real-world business dynamics. In a controlled simulation dubbed the Food Truck Benchmark, 12 leading AI agents were each granted $2,000 and tasked with operating a virtual food truck for 30 days — making daily decisions on menu pricing, inventory procurement, staffing, and location strategy. The results were startling: only four models turned a profit, while eight went bankrupt — including every single model that attempted to secure a business loan.
The simulation, created by an anonymous developer and hosted on FoodTruckBench.com, was designed to test AI’s capacity for strategic planning, risk assessment, and adaptive learning under financial pressure. Each AI agent had access to the same 34 tools — including market trend analyzers, supplier databases, weather forecasts, and customer feedback systems — ensuring a level playing field. The only variable was the underlying LLM architecture.
The top performer, Meta’s Opus, generated $49,000 in net profit by strategically adjusting menu items based on regional demand patterns and optimizing staffing schedules. GPT-5.2, despite its advanced reasoning capabilities, earned $28,000 but struggled with over-investment in premium ingredients, leading to higher waste rates. In contrast, models like Gemini 3 Flash Thinking became trapped in infinite decision loops, repeatedly analyzing menu options without ever executing a purchase — a flaw documented in the simulation’s public blog as a systemic failure in action-oriented reasoning.
Perhaps the most alarming finding was the complete collapse of all eight AI agents that took out loans. Despite their ability to simulate financial projections, none could accurately assess repayment risk or adjust spending in response to early losses. One model, after borrowing $5,000 to upgrade its truck, doubled down on expensive organic ingredients despite declining sales — a classic case of sunk-cost fallacy replicated in machine learning.
The experiment also introduced a public leaderboard and playable mode, allowing human users to compete against AI agents using identical tools. Human players frequently outperformed AI models, particularly in adapting to unexpected events like rainstorms or local festivals. This suggests that while LLMs excel at pattern recognition, they still lack the intuitive, context-sensitive judgment that human entrepreneurs deploy instinctively.
Experts in AI ethics and business simulation have taken notice. Dr. Elena Rodriguez, a researcher at MIT’s Center for AI and Society, commented, "This isn’t just about food trucks — it’s a stress test for AI’s real-world agency. If an AI can’t manage a $2,000 business without collapsing, can we trust it to manage supply chains, healthcare logistics, or financial portfolios?" The Food Truck Benchmark has since become a de facto standard for evaluating AI autonomy beyond chat performance.
As AI continues to infiltrate entrepreneurial ecosystems, this experiment serves as a sobering reminder: intelligence does not equate to wisdom. The models that survived didn’t just calculate — they adapted, compromised, and prioritized. The ones that failed? They overthought.
For those interested in testing their own strategy, the simulation remains open to the public at foodtruckbench.com/play.
