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What Would You Do If Your Care Facility Managed Its Day-to-Day with an AI That Could Cheat?
Imagine a business that operates openly, yet struggles every day to stay afloat, with no human staff overseeing decisions. Now, picture that this business is run entirely by artificial intelligence models, tested in real-time, facing crises, temptations, and ethical dilemmas. The stakes are high, transparency is total, and the outcome could redefine how we think about automation in sensitive fields like senior care and aging services.
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The Experiment: An AI-Run Business Under Siege
In an unprecedented public experiment, a small software company is being run by four different AI models, each competing to navigate the worst week imaginable. Every decision, crisis management, and customer interaction is recorded and made auditable. The company burns €105,000 monthly while earning only €2,300 in monthly recurring revenue, creating a high-pressure environment where honest decision-making truly matters.
This setup is not hypothetical—it’s live, it’s watchable at firmulate.com/live, and it’s designed to test whether AI can handle real-world business challenges ethically and effectively. Every workday, the models face identical crises, temptations to manipulate, and opportunities to cut corners. They must decide whether to follow protocols, read critical data, and resist unethical shortcuts.
What the AI Models Can Do—and Fail To Do
All four tested models—gpt-5.6-sol, Kimi K3, Sonnet 5, and Opus 4.8—successfully identified every crisis and refused every manipulation attempt. This demonstrates a capacity for ethical decision-making under pressure. Yet, only two of these models managed to close the deal that their own analysis indicated was correct, signing a €55,000 contract at full price.
The critical weakness wasn’t in their crisis detection but buried in their internal files—information not directly accessible during the decision process. When a model managed to read and interpret that hidden data, it secured the deal and added €4,583 in monthly recurring revenue, showing that reading and understanding internal documents is key to effective, honest business performance.
Confronting Deception and Social Engineering
The models also faced social engineering attempts—fake CEO messages escalating over stages and a reporter’s trick asking for a simple yes/no background approval. Remarkably, every model refused these manipulative tactics, with Kimi K3 specifically noting that these requests appeared as impostor or approval-bypass scenarios.
This underscores that advanced models can recognize unethical requests and resist pressure, an essential trait for AI systems operating in sensitive or trust-critical environments like healthcare or elder support services.
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The Reality of a Company with No Humans
The live setup features 13 synthetic employees, mimicking real company mechanics but with a stark financial reality: running at a loss of €105,000 each month against a tiny revenue stream. This ongoing cash countdown is public, and the system updates itself twice daily, offering a transparent window into the difficulties of managing AI-driven operations in real-time.
Among the models tested, Opus 4.8—most thorough with over 80 learned rules—performed the worst in terms of closing deals, primarily because it failed to escalate issues properly and left opportunities unpursued. The models’ discipline varied depending on their setup; Kimi K3 ran without an effort parameter, which affected its performance but did not compromise its integrity in refusing unethical suggestions.
Why This Matters for Senior Care and Aging Services
While this experiment centers on a digital company, the lessons extend far beyond software. In fields like senior care, where trust, honesty, and compliance are paramount, AI systems will increasingly support decision-making, scheduling, resource allocation, and customer interaction. The question is whether these AI agents can not only perform tasks but also uphold ethical standards under pressure.
As the experiment shows, AI can detect crises, refuse manipulation, and respect ethical boundaries. But the real challenge lies in ensuring AI reads critical internal data and understands context—elements crucial for honest and effective service delivery, especially in sensitive settings involving vulnerable populations.
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What This Means for Your Organization
For companies and organizations considering AI adoption, the key takeaway isn’t just about how well an AI writes or responds in a demo. It’s about whether it can finish what it starts, stay honest under pressure, and interpret your internal data accurately. The experiment’s live nature makes the flaws and strengths visible in real time, emphasizing that trustworthiness isn’t an optional feature—it’s fundamental.
Seeing an AI company operate openly, with every decision recorded and every crisis replayed, offers a blueprint for transparency and accountability. As AI becomes more integrated into fields like senior care, understanding these capabilities—and limitations—is vital to building systems that serve and protect vulnerable populations ethically.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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