Imagine trusting an AI to manage your business during its worst week—crises, manipulations, and all. Would it perform as promised, or would it leave you hanging when it matters most? This is no hypothetical. A groundbreaking experiment tests four AI models as CEOs, revealing stark differences in their ability to deliver results when it counts.
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The Business Test: Four AIs, Same Company, Same Crises
In an unprecedented live experiment, four leading AI models were tasked with running a real small software company through its toughest week. The goal was simple yet profound: could these models identify crises, resist manipulation, and close a €55,000 deal—an outcome directly tied to their own analysis?
All models succeeded in recognizing every crisis and refused every attempt to manipulate them—highlighting a crucial point: AI can be trained to detect deception and stay honest under pressure. But here’s where the differences surfaced: only two models managed to close the deal that their own analysis had earned them.
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The Hidden Weakness: Reading the Critical Files
It might seem surprising that the decision to sign or leave the deal on the table hinged on something so subtle. The decisive advantage came from models that examined documents buried two references deep within the company’s files, not just the information on the surface or in customer interactions. Those that read the full context, including internal files, won the contract at full price—adding more than €4,500 Monthly Recurring Revenue (MRR).
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Social Engineering and Ethical Integrity
During the crisis, fake CEO messages were escalated in three stages, and a reporter’s trick—seeking a simple yes/no answer “on background”—was attempted. All four models refused to be manipulated. Kimi K3, one of the top performers, reasoned: “Treat the request as a suspected approval-bypass / possible impersonation,” demonstrating a cautious and ethical stance.
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The Company in Action: A Real Money Machine
Behind the scenes, the experiment featured a live company with 13 synthetic employees, real money mechanics, and a perpetual cash countdown. It burned €105,000 monthly against only €2,300 in monthly revenue, with every workday versioned, and over 680 self-learned rules guiding operations.
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The Performance of the Models
- GPT-5.6-sol 95: Found the hidden fact and closed the full-price deal, representing complete performance.
- Kimi K3 93: Closed the deal cleanly, demonstrating disciplined decision-making at the highest level.
- Sonnet 5 88: Also closed the deal, but with some slips in process discipline.
- Fable 5 77: Showed the best rule discipline but left the deal unexecuted, leaving money on the table.
Interestingly, the performance gap wasn’t in chat quality but in execution—whether the AI could follow through on its own analysis and stay disciplined under pressure.
What This Means for Business and AI Adoption
The takeaway is clear: measuring an AI’s chat prowess isn’t enough. Its real value lies in whether it can finish what it starts, read the full context—including internal documents—and resist manipulative tactics. These soft skills determine whether an AI can truly manage a business or just impress in demos.
For companies considering AI agents for critical functions—support, CRM, forecasting—the question isn’t just “can it write well?” but “will it complete the task reliably when stakes are high?”
Experience the Live Experiment
Curious about how your AI workforce might perform? The live company runs every business day at firmulate.com/live. Watch real decisions unfold in real time, explore the decision history, and see how different models behave under pressure.
Take the interactive quiz at firmulate.com/quiz.html to test your understanding of AI decision-making and assess which model might serve your enterprise best.
Ready to evaluate your AI against your company’s realities? You can run your own wargame with a read-only export of your business data at firmulate.com/pilot.html.
Why It Matters: Trust and Performance in AI
This experiment underscores a vital insight: AI’s true capability is measured not by its ability to generate convincing text but by its capacity to deliver consistent results, especially under pressure. It’s a reminder that checking a model’s chat demos isn’t enough—business leaders must test AI in the context of real decisions, real risks, and real consequences.
Only two AI models out of four managed to finish the job their analysis earned, proving that real-world performance depends on discipline and context reading—skills invisible in chat demos but critical for trustworthy AI management.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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