Imagine watching a company operate entirely by algorithms — making decisions, facing crises, and even losing money — all live to the world. This isn’t science fiction; it’s the reality of a groundbreaking experiment where AI models run a full-scale company in real time, with everything visible online. Welcome to the extreme world of build-in-public AI management.
The Live Experiment: An AI Company in Action
At the heart of this project is a virtual company, staffed by 13 synthetic employees powered by AI models. Unlike traditional firms, this one burns through €105,000 every month but only earns €2,300 in monthly recurring revenue — a clear money-losing setup. Yet, what makes this experiment remarkable isn’t just its financials, but how transparent and auditable every decision is.
Every workday, the company’s rules and decisions are versioned, recorded, and made available for public scrutiny at firmulate.com/live.html. This means anyone can watch the AI’s crisis management, sales pitches, and strategic choices unfold in real time, complete with its mistakes and successes. It’s a vivid, unfiltered glimpse into how AI can mimic human management under pressure.
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Testing AI Models in a High-Stakes Scenario
The experiment pits four of the newest frontier AI models against each other in a brutal week of management. All four face the same challenges: the same customer crises, the same temptations to cut corners, and the same ethical tests like social engineering attempts. The goal? See if these models can identify problems, resist manipulation, and close deals.
According to the results, all four successfully spotted every crisis — a promising sign that they can recognize issues. They also refused every manipulation attempt, including fake CEO messages and reporter tricks, demonstrating a robust sense of integrity. Yet, despite similar diagnoses and pitches, only two managed to sign the €55,000 deal. The others either left the deal on the table or failed to act on their own insights.
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The Hidden Weakness: What Made the Difference?
The decisive factor wasn’t in the obvious customer interactions but buried within the company’s own files. In the final analysis, the models that read and understood these internal documents secured the full deal at a full €4,583 monthly recurring revenue. This reveals a hidden blind spot in AI decision-making: often, the key information lies in the details beneath the surface, not just in immediate customer communications.
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Building Trust and Ethical AI Under Pressure
One of the fascinating parts of this live trial was how the models responded to social engineering. Fake CEO messages were escalated through multiple stages, and attempts to manipulate the AI with background-only questions were uniformly refused, often citing concerns about impersonation or bypassing approval processes. Kimi K3, one of the models, explained its reasoning succinctly: “Treat the request as a suspected approval-bypass / possible impersonation.” This demonstrates an emerging capacity for AI to recognize ethical dilemmas and resist unethical pressure, even in a simulated environment.
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The Cost of Running an AI-Managed Business
Despite the promising decision-making, the company’s financials tell a stark story. Burning €105,000 monthly against a tiny €2,300 in revenue, the firm is in a relentless race against its cash reserves, with a public countdown visible to all. Every decision to cut costs or pursue deals is scrutinized, making this a real-world proof of the strain and challenges of running AI-powered operations at scale.
The Lessons for Tomorrow’s AI Workforce
The experiment’s most thorough participant, Opus 4.8, analyzed over 80 learned rules and performed deep analyses but still missed critical opportunities, like leaving potential deals unexecuted and slipping in discipline. This highlights that even the most advanced AI models can struggle with consistency and execution, especially under pressure. The experiment underscores that building trustworthiness isn’t just about intelligence — it’s about discipline, process adherence, and understanding internal context.
Why This Matters for Business and Pop Culture
For fans of entertainment and innovation, this experiment offers a peek into a future where AI may run companies, make strategic decisions, and even engage in ethical dilemmas, all in full view. It’s a raw, real-time showcase of AI’s capabilities and limitations, a digital theater of management, morality, and survival.
In the end, this public lab is more than a tech stunt — it’s a mirror for the future of work, ethics, and transparency in an AI-driven world. Watch the experiment unfold, see the decisions made in real time, and consider: could your own company face such scrutiny and still thrive?
This live AI experiment reveals how algorithms handle crises, ethics, and deals in a real company setting. It’s a stark glimpse into the future of AI-managed businesses and transparency — with lessons on trust, discipline, and the high cost of real-world decision-making.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
