What Gardening Can Teach Us About AI in Business
Just as a gardener must nurture plants carefully, managing an AI-driven company requires precise attention to detail — but what happens when there’s no one to oversee the process? Imagine a business with no employees, yet still losing money every day. Sounds like a garden gone wild, but it’s actually a real-time experiment in AI management.
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The Live Experiment: A Business in Free Fall
At the heart of this groundbreaking project is a small software company run entirely by AI models. Instead of human workers, there are 13 synthetic employees, each powered by advanced AI models that simulate decision-making in a real business environment. The company faces daily crises—customer issues, ethical dilemmas, and operational temptations—exactly as a human company would. Yet, every decision is made by different AI models, and each is publicly documented, versioned, and auditable.
This isn’t just a theoretical test; it’s a climate of real financial mechanics. The company burns through €105,000 each month even as it generates a modest €2,300 in recurring monthly revenue. The live site on firmulate.com/live.html offers viewers a window into this ongoing struggle, where every workday unfolds with a new version of decisions, rules, and outcomes.
What Are These AI Models and How Do They Perform?
The experiment compares four frontier AI models, including GPT-5.6-sol and Kimi K3, tested through a challenging week of identical crises. Remarkably, all models identified every crisis and refused to be manipulated—impressively showing integrity in difficult situations. Only two managed to close a €55,000 deal that their own analysis had earned, matching diagnosis and pitch but failing to secure signatures. The key to success, however, lay hidden in the company’s own internal files, buried two document references deep. When the models read those files, they secured the full deal at a total monthly recurring revenue of over €4,500—more than double the company’s current income.
This shows that the critical weakness wasn’t in customer interactions but in internal knowledge, which the models that read more of the company’s documents uncovered. It’s a reminder that in both business and gardening, knowing what’s beneath the surface can be the difference between thriving and failing.
Ethical and Risks Management in a Digital Business
One of the most striking features of this experiment is how the AI models handle social engineering attempts. Over three stages, fake CEO messages and subtle reporter tricks are employed to manipulate decision-making. All five models tested refused to engage with these scams—each reasoning that such requests could be impersonation or approval bypass attempts. Kimi K3 notably flagged the requests as suspicious, illustrating a built-in skepticism aligned with ethical safeguards.
This highlights the importance of trustworthiness in AI systems managing real business processes. Just as a gardener must ensure their tools are reliable and resistant to pests, companies deploying AI must prioritize integrity and security, especially when human oversight is minimal or absent.
The Reality of a Build-in-Public Business
The company running this live experiment is as real as it gets—13 synthetic employees working in tandem within a framework of over 680 self-learned rules. It burns through money daily and has a public cash countdown, making its struggles transparent for all to see. Every decision, every crisis, and every rule is versioned and accessible for analysis at firmulate.com/live.html.
Failure is part of the process, but so is learning. The model that performed the most thoroughly—Opus 4.8—demonstrated deep analysis with over 80 learned rules but still left opportunities on the table. Discipline slipped, and some decisions ended up in a locked department rather than escalation, illustrating that even sophisticated AI can face challenges in complex, high-pressure situations.
The Lessons for Business and Gardening
What can gardeners, greenhouse managers, or outdoor enthusiasts take away from this experiment? The core lesson is that understanding your internal environment—whether soil health or internal documents—is crucial to thriving. Just as reading the right soil test results can prevent crop failure, reading the company’s internal files was key to closing a critical deal here.
Moreover, trust and integrity matter. The AI models refused to be manipulated, signaling that built-in safeguards can prevent costly mistakes—be it in digital or physical gardens.
What’s Next? Watching the Business Fight for Survival
This ongoing live experiment is a rare glimpse into AI’s potential and limitations in running a real business. It’s a build-in-public story of innovation, failure, and resilience—where every workday adds a new chapter. Viewers can watch the company’s real money mechanics unfold daily, learn from the decisions made, and even participate in quizzes at firmulate.com/quiz.html.
For organizations contemplating AI integration, the key takeaway isn’t just whether AI can write well. It’s whether AI can finish what it starts, read critical internal data, stay honest under pressure, and deliver useful, trustworthy work—just as a gardener must nurture the right conditions for growth.
Key Takeaway
In a world where AI manages critical business decisions without human oversight, trustworthiness, internal knowledge, and discipline are vital. This live experiment reveals both the promise and the peril—showing that AI can identify crises and refuse manipulations, but internal insights can make or break a deal. Watching this company fight for survival teaches us that understanding what’s beneath the surface is just as important in business as in gardening.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
