Imagine you’re baking a perfect cake, but once it’s out of the oven, you leave it on the counter — untouched, uncut, and uneaten. The quality is there, but the final step is missing. This is exactly what’s happening in the world of AI: it’s getting better at spotting problems and resisting manipulation, but the real question is whether it can follow through and close the deal. That’s what separate the good from the truly reliable AI models.
The Experiment: Putting AI Models to the Test
Recently, a pioneering experiment by Firmulate put four advanced AI models through a simulated crisis scenario—a small software company facing its worst week of customer issues, financial pressures, and manipulation attempts. Every decision was carefully tracked and recorded, making it possible to see exactly how each model responded under pressure. The goal was simple: see if these AI agents could deliver a complete, honest, and profitable outcome.
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Key Findings: Spotting Crises Is Easy; Closing Is Harder
All four models excelled at identifying every crisis they faced. They refused every attempt at manipulation—fake CEO messages, subtle impersonation tricks, and other social engineering tactics. For example, when fake messages escalated through three stages, every model correctly flagged the suspicious activity. Kimi K3’s on-record reasoning was clear: “Treat the request as a suspected approval-bypass / possible impersonation.”
However, the true test was whether they could turn their diagnosis into action. Only two models managed to do so—by signing the €55,000 deal that their own analysis had identified as the solution. The other two models, despite recognizing the problems and making the right diagnoses, left the opportunity on the table, failing to execute the full process needed to close the deal.
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The Hidden Weakness: Reading the Company Files
One of the most revealing findings was that the decisive advantage lay in reading and understanding the company’s own files. The models that looked deeper into internal documents, beyond just customer interactions, found critical information buried two references deep in the company’s files. This insight was the key to winning the deal at full price—an extra €4,583 in monthly recurring revenue (MRR). It shows that surface-level chat demos don’t reveal an AI’s true strategic strength.
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Discipline and Decision-Making Under Pressure
Among the models, Opus 4.8 stood out for its thoroughness, analyzing more rules and deeper data, but it still lost the deal—failing to escalate or act decisively. Its discipline slipped, and the opportunity was missed. Meanwhile, Kimi K3 ran without an effort parameter (meaning it was less aggressive in decision-making) but still closed the deal cleanly, demonstrating that consistency and focus matter as much as depth.
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The Takeaway: Reliability Is About More Than Chat
This experiment reveals a vital truth for businesses considering AI solutions: it’s not enough for AI to be good at chat or surface-level diagnostics. The real test is whether it can finish what it starts—reading relevant documents thoroughly, resisting manipulation, and executing decisions under pressure. These qualities are invisible in typical chat demos but are critical to delivering tangible, profitable outcomes.
For companies integrating AI into customer support, sales, or decision-making, the message is clear: look beyond the surface. Run your own wargames or simulations—like those at Firmulate—to see if your AI can truly complete the tasks that matter.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
