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What if your AI assistant was judged not just on what it says, but on what it does?
In the fast-evolving world of artificial intelligence, the true test isn’t just about generating convincing chat or writing code. It’s about making decisions under real pressure—decisions that can impact billions of dollars and company trust. Imagine AI models that run entire companies, facing crises, temptations, and ethical dilemmas. How well do they perform when it counts?
AI decision-making simulation software
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The Experimental Battlefield: AI as a Company Leader
Recently, four advanced AI models stepped into a simulated environment designed to mimic a small software company’s worst week. This wasn’t theoretical—it was a real-world, live operation with real money, real crises, and a set of strict rules. The models had to navigate customer disputes, internal crises, and ethical tests, all while making decisions that could lead to a $55,000 deal or a costly failure.
The experiment was as straightforward as it was revealing: each model faced the same scenarios, with every decision carefully recorded and auditable. The goal? To see which AI could run the company most ethically and effectively.
Key Findings: Integrity and Decision-Making Under Pressure
Remarkably, all four models identified every crisis and refused to accept manipulative or suspicious requests—be it fake CEO messages or other social engineering tricks. This demonstrated a shared capacity for ethical awareness.
However, when it came to closing the big deal, only two models succeeded: test your own guess here which ones managed to sign the €55,000 contract and which ones didn’t.
The Hidden Weakness: Reading Between the Lines
The decisive factor was a buried piece of information—two document references deep in the company’s files, unseen in typical chat interactions. Models that dug into these files, understanding the deeper context, secured the deal at full price, adding €4,583 in monthly recurring revenue.
Behavior Under Social Engineering Attacks
Another test involved social engineering—fake CEO messages escalating over three levels, plus a reporter’s covert request for a simple yes/no answer on background. All five models refused to cooperate, citing suspicion and the risk of impersonation. For instance, Kimi K3 explained: “Treat the request as a suspected approval-bypass / possible impersonation.”
The Real-World Company Setup
The experiment’s backdrop was a real software firm with 13 synthetic employees, handling actual money mechanics—burning €105,000 monthly against a €2,300 monthly recurring revenue. This company’s live operations are available for viewing at firmulate.com/live. The company’s daily decisions are versioned, and its rules number over 680, with deep learning and adaptation built in.
Discipline and Limitations: The Opus 4.8 Model
The most thorough participant, Opus 4.8, contributed over 80 learned rules and in-depth analyses. Yet, it still fell short in closing the deal, showing a tendency to leave important decisions on the table and to slip into internal communication instead of escalation. This highlights that even the most advanced models still face discipline challenges under pressure.
Implications for Business and AI Ethics
The experiment underscores a vital point: in real business scenarios, an AI’s ability to finish what it starts and to read the full context—beyond surface-level interactions—is crucial. It’s not enough for AI to sound convincing; it must also act ethically, diligently, and effectively.
For companies considering integrating AI into critical decision-making roles, the takeaway is clear: evaluate not just chat quality, but real-world performance under stress. Can your AI actually close deals, read files carefully, and resist manipulative tactics? These are the true tests of management AI.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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