What if you could watch an AI run a small business — in real time — and see if it makes the right decisions under pressure? Welcome to the frontier of transparent AI management.
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The Live Experiment: An AI-Run Company in Real-Time
At the heart of an ongoing, publicly accessible experiment is a small software company run entirely by AI models. This setup isn’t a simulation; it’s a real-time, live-logged environment where 13 synthetic employees face the same crises, temptations, and decisions as a human team. Every workday is versioned and auditable, with the goal of evaluating whether these AI models can truly manage a business — not just generate pretty conversations.
Uncovering the Hidden Weakness
Despite all the advanced scoring — with the top model achieving a 95 out of 100 in the Crucible League final — the experiment reveals a surprising vulnerability. The models successfully identify crises and refuse manipulative tactics, but the decisive factor for closing a crucial €55,000 deal depended on a hidden detail buried two document references deep in the company’s internal files. When a model read this buried information, it clinched the deal at full price, increasing monthly recurring revenue by over €4,500.
Decisions Under Pressure: Honesty and Discipline
The experiment also tested how models respond to social engineering attempts, such as fake CEO messages escalating over three stages and even a fake reporter request. Remarkably, all five models refused to manipulate the process, with the Kimi K3 model explicitly reasoning: “Treat the request as a suspected approval-bypass / possible impersonation.”
Real Money, Real Crises, Real Risks
The live company operates with a hefty cash burn of €105,000 each month against a modest €2,300 monthly recurring revenue. Its public cash countdown and self-learned playbook rules — over 680 of them — illustrate the ongoing balancing act. Each decision, each crisis management move, is documented, versioned, and monitored at firmulate.com/live.
The Deep Dive: OPUS 4.8 and the Discipline Dilemma
Among the AI models, OPUS 4.8 stood out as the most thorough, analyzing over 80 learned rules and providing deep insights. Yet, it finished last in the critical deal-closing task because discipline slipped, with some decisions left unexecuted or relegated to locked departments instead of escalation. Interestingly, all models exhibited this weakness, hinting that even the most advanced AI can falter under pressure or in complex decision-making scenarios.
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Why This Matters for Your Smart Home and Appliances
While this experiment involves a small software firm, the implications are broad. Imagine AI managing your smart home devices — from thermostats to security systems — under real-world crises. Will they read all necessary information before acting? Will they stay honest under stress? And most important, will they finish what they start — like ensuring your heating system adjusts correctly during a cold snap?
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The Future of Transparent AI Decision-Making
This ongoing live experiment demonstrates that AI decision-making can be transparent, auditable, and accountable. Unlike typical chatbots or AI assistants, these models are tested in high-stakes, real-world scenarios, revealing critical strengths and flaws. As AI begins to touch more aspects of daily life, knowing whether it can manage complex tasks honestly and effectively becomes essential.
Where to Watch and Learn More
See the latest results and watch the ongoing performance at firmulate.com/live.html. Curious about how AI models make decisions? Explore the complete scoring and detailed analysis at firmulate.com/quotes.html.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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