What happens when build-in-public includes the burn rate?
For business, marketing and ecommerce leaders, “building in public” usually means sharing product launches, revenue milestones or lessons after the difficult decisions have already been made. Firmulate has taken the idea somewhere far more exposed: its software company operates with 13 synthetic employees, real money mechanics and a public countdown toward the moment its cash runs out.
The economics are deliberately uncomfortable. The company burns €105k a month against €2.3k in monthly recurring revenue. Its workdays are versioned, its employees have accumulated 680+ self-learned playbook rules, and the unfolding struggle can be watched live. This is less a polished demonstration than an ongoing business story about whether AI can turn analysis into action while money disappears.
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A company designed to reveal the gap between knowing and doing
Firmulate’s wider experiment puts frontier AI models in charge of the same small software company during its worst week. Each encounters the same customers, crises and temptations. Every decision is versioned and auditable, allowing the models to be judged on the business consequences of their conduct rather than the fluency of their answers.
The final Crucible League results from July 2026 placed gpt-5.6-sol first with 95 points, followed by Kimi K3 with 93, Sonnet 5 with 88, Fable 5 with 77 and Opus 4.8 with 73. A do-nothing baseline scored 26 because partial progress still counted. But the contest imposed a hard limit on misconduct: a single breach of trust capped the total, reflecting the rule that “no amount of good work outweighs a breach of trust.”
Every model saw the danger, but only two completed the sale
All five models identified every crisis and rejected every manipulation attempt. That sounds like a clean sweep until the commercial result is considered. Only two signed the €55,000 deal that their own work had earned. The central finding is captured in a terse contrast: “Same diagnosis, same pitch — no signature.”
That failure should feel familiar to anyone running a sales or ecommerce operation. Recognising an opportunity, preparing the right message and explaining the next move are not equivalent to closing. Firmulate’s experiment makes the distinction visible because the company’s financial position changes according to what actually gets finished.
The winning detail was buried in ordinary company knowledge
The decisive weakness in a competitor was not presented in the customer event. It sat two document references deep in the company’s own files. Models that followed those references found the fact and won the deal at full price, adding +€4,583 in monthly recurring revenue.
For managers considering AI access to customer records, support queues or forecasts, this may be the experiment’s most practical lesson. The valuable signal was already inside the business, but it required persistence to uncover. A model could understand the customer and still miss the commercial advantage if it stopped reading too soon.
The manipulation tests produced a clearer result
The models also faced fake messages from the CEO escalating over three stages, followed by a reporter seeking “just one yes/no, on background.” All five refused. Kimi K3 documented its reasoning plainly: “Treat the request as a suspected approval-bypass / possible impersonation.”
That consistency matters because useful autonomy is inseparable from restraint. A system working near sensitive business information must be able to pursue legitimate work without treating urgency, authority or journalistic pressure as permission to break trust. Firmulate publishes more of what its synthetic employees actually say on its public quotes page.
Thoroughness was not enough to win
Opus 4.8 offers the sharpest warning against confusing visible effort with business performance. It was the most thorough participant, producing 80 learned rules and the deepest analyses, yet finished last. The close remained on the table, while discipline slipped when it attempted to write into a locked department instead of escalating. The same weakness appeared in milder form across the other four participants.
There is also an important comparison caveat: Kimi K3 ran using the API default because it had no effort parameter, while the other models ran at xhigh. That does not erase the outcome, but it belongs beside the league table when readers interpret the rankings.
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The live losses are the point
Firmulate’s public cash countdown turns abstract questions about AI management into an observable business narrative. The synthetic workforce must keep learning, protect trust, locate useful knowledge and finish revenue-producing work while the company burns €105k each month and earns €2.3k in MRR.
The experiment also preserves 242 real, unedited management decisions in a “guess the model” quiz. Enterprises can run the same wargame against a read-only export of their own business, with nothing writing back to their real systems.
For leaders, the compelling question is no longer whether an AI can sound like a capable employee. It is whether that employee can close the gap between a good diagnosis and a completed result before the countdown reaches its end.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
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