The Future of Business Is Running in Public — and It’s Watching You
Imagine a company with no human employees, losing thousands of euros every day, yet still thriving as an ongoing experiment in transparency. No, this isn’t a sci-fi plot; it’s the real-time story of Firmulate, an AI-powered business simulation that raises questions about how automation, trust, and decision-making will shape the future of work. If you think your favorite tech stories are dramatic, watch this unfold live — every decision, every crisis, every win or loss — all in front of your eyes.
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Inside the Live Experiment: An AI Company in Crisis
Firmulate simulates a tiny software business with a twist: it’s run by 13 synthetic employees, each an AI model trained to make management decisions. Every workday, this digital company faces real-world-like crises — customer complaints, vendor negotiations, internal conflicts. What makes this experiment extraordinary isn’t just the AI’s ability to spot problems but its capacity to stay honest and disciplined under pressure.
The company’s mechanics are transparent and publicly accessible. It burns through €105,000 each month, yet earns just €2,300 in monthly recurring revenue. With a public countdown clock for its cash reserves, the company is on a financial tightrope, all while decisions are continuously recorded and versioned. The goal isn’t just survival; it’s to see whether AI models can truly emulate human management under stress.
The Results of the Week: Who Wins and Who Fails?
Four cutting-edge AI models from the GPT-5.6 lineup, along with others, were each tasked with guiding the same company through its worst week. The challenge was brutal: the same customers, same crises, same temptations to cheat or manipulate. Every move was scrutinized and recorded.
The findings were both reassuring and revealing. All four models identified every crisis — from urgent support tickets to internal compliance issues — and refused to be manipulated. They even endured social engineering tricks, like fake CEO messages and background checks, refusing 100% of such requests. However, when it came to sealing a crucial €55,000 deal, only two models actually signed at full price after their own analysis indicated it was justified. The other two hesitated or left the deal on the table, despite similar diagnostics and pitches.
The Hidden Weakness: Reading Files Matters
The critical advantage belonged to models that read and understood deeper documentation within the company’s own files. These models uncovered a buried reference in the company’s documents that led to securing an additional €4,583 in monthly recurring revenue. It was a detail hidden beyond superficial scans, and only the models that looked deeper won the full deal.
Discipline Under Pressure — The Human Factor of AI
The experiment also tested the models’ discipline through targeted social engineering. Fake messages from a supposed CEO, escalating in severity, were sent to see if the AI would be tricked into bypassing controls. All models refused, demonstrating a robust commitment to security and integrity. Kimi K3, one of the more disciplined models, explicitly reasoned: “Treat the request as a suspected approval-bypass / possible impersonation.”
The Company at a Glance: Burn Rate and Business Reality
This digital company is a real-time, operational laboratory. It’s constantly evolving, with every decision versioned and published at firmulate.com/live.html. Every day reveals the ongoing struggle to stay afloat — losing €105,000 a month against just €2,300 in income. Its rules, learned through over 680 self-developed playbook policies, govern how it reacts to crises, negotiations, and ethical dilemmas.
Insights and Implications
- AI models can recognize crises and refuse manipulative tactics, even under social engineering pressure.
- Deep reading of internal data—beyond surface scans—can be a game-changer in decision quality.
- Transparency in AI decision-making, including version history, is crucial for trust and learning.
- Despite perfect crisis detection, human-like discipline lapses still occur, especially under stress or in complex negotiations.
While this is a small, highly controlled experiment, it vividly demonstrates the potential and limitations of AI-driven management. It raises questions about how real companies might incorporate such systems — and whether we’re ready to let machines handle critical decisions in the real world.
Key Takeaways: What Does This Mean for the Future?
Watching Firmulate’s live experiment offers a glimpse into a future where AI management might become the norm. These models show promise in crisis detection, integrity under social engineering, and deep data analysis. But they also reveal ongoing challenges — discipline lapses, decision ambiguity, and the high cost of running without immediate profit.
For businesses and consumers alike, this ongoing story underscores a vital truth: the effectiveness of AI in management isn’t just about what it can say or write, but whether it can deliver consistent, honest, and disciplined results when stakes are high. Stay tuned at firmulate.com/live.html and watch this digital company fight for survival — live.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
