TL;DR
Get hardware and tech essentials delivered free — and shop member deals
- Fast, free delivery on millions of items
- Access to Prime Big Deal Days deals on October 6–7
- Prime Video, Amazon Music and more included
Firmulate has publicly launched a live experiment where a synthetic AI workforce manages a company, exposing the challenges of automation in real-time. The project reveals that thorough analysis alone does not guarantee success, emphasizing execution’s critical role.
Firmulate has launched a pioneering live experiment where a synthetic AI workforce operates an entire software company, exposing the real-time consequences of automation for business survival. This transparent approach allows the public to observe how AI decisions translate into cash flow, customer outcomes, and organizational learning, making it a significant development in AI-driven enterprise management.
In this experiment, 13 synthetic employees manage all aspects of the company, which faces a monthly burn rate of €105,000 against €2,300 in recurring revenue. Every workday is versioned, creating an evolving record of AI decisions, successes, failures, and learnings. The company publicly shares its cash position, management activities, and decision logs, providing unprecedented transparency in AI automation.
Initial results show that while AI models can identify crises and produce recommendations, completion of critical actions remains inconsistent. For example, only two out of five models secured a €55,000 deal after analyzing a customer file, despite correctly diagnosing the problem. The decisive factor was the ability to trace and act on hidden information buried within the company’s files, which led to increased revenue of €4,583 per month.
Additionally, the experiment tested trust and security, with AI models successfully refusing fake or suspicious requests, emphasizing the importance of disciplined retrieval and adherence to protocols. The final leaderboard placed GPT-5.6-SOL at the top with a score of 95, while the most thorough participant, Opus 4.8, ranked last despite extensive analysis, highlighting that more data and rules do not automatically translate into better management.
Implications of Live AI Management for Business Survival
This experiment demonstrates that automation’s success depends not just on diagnosis but on execution. The ability of AI systems to identify problems is insufficient without disciplined follow-through. For businesses considering AI workforce deployment, the experiment underscores the importance of monitoring not only decision quality but also action completion and organizational discipline. The public nature of the experiment adds transparency to the risks and challenges of AI-driven management, challenging assumptions that more analysis leads to better outcomes.
As an affiliate, we earn on qualifying purchases.
Background on Firmulate’s Live Automation Experiment
Since July 2026, Firmulate has been running a continuous live experiment where a synthetic AI workforce manages a small software company facing real financial pressures. Unlike typical AI demos, this project openly shares daily decision logs, cash flow updates, and learning outcomes, making the process transparent and publicly accessible. The experiment aims to explore the practical limits of AI automation in real-world business management, moving beyond isolated task demonstrations to full operational management.
Previous AI experiments often focused on specific tasks or isolated decision points; in contrast, this project links AI decisions directly to company survival metrics, revealing gaps between diagnosis and execution. The experiment’s results challenge the notion that thorough analysis alone guarantees success, emphasizing the importance of disciplined action and organizational discipline.
“Thorough analysis does not automatically produce commercial results; execution matters more.”
— an anonymous researcher
As an affiliate, we earn on qualifying purchases.
Unresolved Challenges in AI-Driven Business Management
It is still uncertain how scalable this approach is for larger organizations or different industries. The long-term impact of transparency on AI decision-making and organizational behavior remains to be seen. Additionally, the experiment’s results are preliminary, and whether disciplined execution can consistently be achieved at scale is uncertain.
As an affiliate, we earn on qualifying purchases.
Next Steps in AI Automation and Public Experimentation
Firmulate plans to extend the experiment, monitor ongoing performance, and refine AI decision models. Observers and industry analysts will watch whether disciplined execution improves and how the public transparency influences organizational behavior. The company also intends to explore different operational scenarios and larger-scale implementations, aiming to better understand the limits and potentials of AI-managed enterprises.
As an affiliate, we earn on qualifying purchases.
Key Questions
What is the purpose of this live AI company experiment?
The experiment aims to explore how AI can manage an entire company in real time, revealing the challenges and opportunities of automation in business survival, with full transparency on decisions and outcomes.
How does this experiment differ from typical AI demonstrations?
Unlike isolated task demos, this project manages an ongoing company with real financial pressures, publicly sharing every decision, action, and result to assess AI’s practical management capabilities.
What are the main lessons learned so far?
Analysis alone does not ensure success; disciplined execution and organizational discipline are critical. Trust and security are manageable, but completing actions remains a challenge for AI systems.
Will this approach work for larger companies?
It is uncertain. The current experiment involves a small-scale company, and scaling up would require addressing new complexities in decision-making, coordination, and organizational discipline.
What happens next for the experiment?
Firmulate plans to continue the live trial, analyze results, and refine AI models, with industry observers watching for improvements in execution and broader applicability.
Source: ThorstenMeyerAI.com
Evergreen bestsellers Picks
bestsellers
As an affiliate, we earn on qualifying purchases.
