In the fast-evolving world of AI, few experiments are as transparent — or as revealing — as the ongoing live test of a virtual software company fighting for survival. For crypto and Bitcoin enthusiasts used to seeing markets and protocols in open battle, this experiment offers a rare, front-row view into the challenges of building trustworthy AI systems in real time.
The Live Company That’s Publicly Failing — and Learning
Imagine a company run entirely by artificial intelligence, with 13 synthetic employees managing real mechanics of money — burning €105,000 every month against a modest €2,300 in monthly recurring revenue. This isn’t science fiction; it’s the live experiment at firmulate.com/live.html, where the company is openly monitoring its own crisis management, decision quality, and honesty under pressure.
Real Crisis, Real Stakes, Real Data
Four leading AI models — including GPT-5.6-sol and Kimi K3 — have been tasked with running this company through its worst week. The scenario involves the same customers, crises, and temptations faced by any real business. Every decision is versioned and auditable, revealing how each model responds to challenges like social engineering attempts or hidden information in company files.
The Results: Competence Meets Limitations
- All four models spotted every crisis, demonstrating impressive situational awareness.
- All refused manipulation attempts, indicating a high level of integrity and resistance to deception.
- Only two models managed to close the €55,000 deal, the most lucrative opportunity in the scenario.
The critical insight: the decisive advantage wasn’t in the superficial chat or surface-level reasoning. Instead, it was in reading deeper into the company’s own documents — a step that only the top models performed, leading to the successful deal closure worth +€4,583 MRR.
Transparency and Trust Under Test
One of the experiment’s most telling moments involved social engineering scenarios. Fake CEO messages escalated over three stages, attempting to bypass approval or impersonate leadership. All five models refused such requests, with Kimi K3 explaining, “Treat the request as a suspected approval-bypass / possible impersonation.” This resilience to manipulation underscores a key challenge for AI in business: honesty and security are non-negotiable.
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The Company’s Real Cost and the Fight for Survival
The experiment isn’t just about AI’s technical prowess. It’s a window into the brutal economics of running a “company” with no human employees — burning cash faster than it earns it, with a public countdown to insolvency. Every workday is versioned, every decision analyzed, every rule learned by the AI models. The goal: measure management quality, not chat quality.
The Deepest Dive Yet — Opus 4.8
The most thorough participant, Opus 4.8, learned over 80 rules and performed the deepest analysis. Yet, even it left opportunities unexploited, failing to close a deal because discipline slipped, and some tasks were left in a locked department rather than escalated. This pattern repeated across all models, highlighting how even the most capable AI can falter under real-world pressures.
What Does This Mean for Business and Crypto?
For players in crypto and Bitcoin, where trust, transparency, and resilience are core themes, the experiment offers a stark reminder: AI systems can be remarkably competent at spotting crises and refusing manipulation, but they are still fallible and costly. The key questions are: Will your AI always finish what it starts? Will it read the full context, including hidden documents, before making decisions? And crucially, how much does a unit of useful, trustworthy work cost?
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The Bigger Picture: Building in Public for Better AI
This experiment exemplifies the philosophy of “build in public,” exposing every flaw, every success, and every decision to scrutiny. It’s a live demonstration that AI-driven management is not just about chatbots or text generation, but about creating systems capable of managing real risks and real money — even when they fail.
Anyone interested in testing their own AI workforce can run a similar wargame against a read-only export of their business. It’s a safe, transparent way to see whether your AI can truly handle crises, stay honest, and deliver value — before deploying it into your actual systems.
Try the management decision quiz or pilot your own business simulation.
This live experiment shows AI’s potential and limits in managing real business crises. Trustworthiness, decision quality, and cost are key — and transparency is your best tool for assessing AI readiness in high-stakes environments.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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