What Can a Company Run by AI Teach Us About Trust and Resilience?
Imagine witnessing a small software company operated entirely by artificial intelligence — with no human employees — navigate a week full of crises, temptations, and tough decisions. Now, imagine doing that live, watching every move, every decision, and every slip-up in real-time. This is not science fiction but the core of an extraordinary ongoing experiment that pushes the boundaries of transparency in business and AI.
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The Live Company: An AI-Driven Business in the Spotlight
At the heart of this experiment is Firmulate, a public platform that runs a simulated company with 13 synthetic employees. This isn’t just a marketing stunt; it’s a real-time, measurable test of how AI models handle complex management tasks, financial mechanics, and crises. The company operates with a monthly burn rate of €105,000 against a revenue of just €2,300, presenting a stark picture of survival challenges.
Every workday, the company’s decision-making process is versioned and publicly accessible, with over 680 self-learned playbook rules guiding its operations. The live site, firmulate.com/live.html, provides a window into this world, showing the company’s current state, ongoing runs, and the decisions being made.
Testing AI Models Through a Common Crisis
The core experiment involves four frontier AI models—each tasked with running the same small software business through its worst week. The scenarios include customer crises, internal misjudgments, and external manipulations. These models face identical challenges, which are carefully documented and versioned, ensuring transparency and reproducibility.
The findings are revealing:
- All four AI models identified every crisis that arose.
- Each refused manipulation attempts, including social engineering tactics like fake CEO messages and reporter tricks.
- Only two models successfully closed a €55,000 deal based on their own analysis, demonstrating a capacity to read and act on critical information buried deep within documents, not just surface-level cues.
Behind the Curtain: The Hidden Weakness
The key to winning the deal was reading a specific document reference in the company’s files. Those models that analyzed the deeper information, rather than just surface cues, secured the deal at full price—adding €4,583 MRR to the company’s runway. Conversely, the other models missed this crucial detail, highlighting how deep analysis and thorough reading are vital in real management scenarios.
The Human-Like Failings of AI
Interestingly, the most disciplined AI, Kimi K3, ran at a default effort level and avoided risky shortcuts, yet still failed to close the deal. The most comprehensive model, Opus 4.8, with over 80 learned rules, slipped up by leaving a close opportunity unexploited and not escalating issues properly. These failures underscore that even the most advanced models exhibit human-like flaws under stress.
Security and Ethical Considerations
The experiment also tested social engineering resistance. All models refused staged manipulative requests, including escalating fake CEO commands and journalist tricks—showing a robust understanding of trust and impersonation risks. Kimi K3 explicitly treated such requests as suspicious, demonstrating a cautious, security-minded approach.
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Why This Matters for Business and AI
This ongoing, transparent experiment offers a rare glimpse into how AI can perform in real-world management conditions. It highlights that success isn’t just about generating coherent chat responses but about completing critical tasks, understanding nuanced information, and resisting manipulation—all under pressure.
For companies considering AI integration, the lessons are clear: it’s essential to test AI models against real business scenarios before deploying them in live environments. The firmulate.com/quiz.html quiz, based on 242 actual management decisions, emphasizes just how nuanced human judgment remains in the face of AI capabilities.
Moreover, the platform’s pilot program allows businesses to run their own internal simulations, providing a risk-free environment to evaluate AI performance before committing real resources. This build-in-public approach demonstrates how transparency can help build trust in AI systems, especially when they are tasked with critical management functions.
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Building Trust in AI: More Than Just Chat
The ongoing live experiment by Firmulate shows that AI’s true strength lies in its ability to read deeply, resist manipulation, and make consistent decisions under pressure. For businesses, the takeaway is clear: rigorous testing in real scenarios is vital. Transparency and build-in-public strategies can help ensure AI systems become trustworthy partners rather than unpredictable risks.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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