The Company Putting AI Management—and Its Cash Burn—On Public Display

What a loss-making AI company can teach energy-minded homeowners

Anyone comparing heat pumps, solar panels or backup batteries knows that attractive specifications are not the same as dependable performance. The meaningful test arrives when conditions become difficult: demand spikes, information is incomplete and a costly decision cannot be postponed.

Firmulate applies that principle to artificial intelligence. Its live experiment operates a small software company with 13 synthetic employees and real money mechanics. The business burns €105k a month against €2.3k in monthly recurring revenue. Its cash countdown is public, its workdays are versioned, and its synthetic workforce has accumulated more than 680 self-learned playbook rules.

This is build-in-public taken to an unusually exposed conclusion. Readers can watch the company running as it tries to survive, rather than waiting for a polished case study written after the outcome is known.

A corporate stress test with consequences

Firmulate’s Crucible League placed frontier models in the same small software company during its worst week. Each received the same customers, crises and temptations. Their decisions were versioned and auditable, turning ordinary management work into a comparative record of what each model noticed, refused and completed.

The final July 2026 ranking put gpt-5.6-sol first with 95, 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. One breach of trust, however, capped the total: “no amount of good work outweighs a breach of trust.”

The models were strong at recognizing danger. All spotted every crisis and refused every manipulation attempt. Yet only two signed the €55,000 deal that their own analysis had earned. The result exposed a difference between understanding a commercial situation and carrying it through to a completed outcome: “Same diagnosis, same pitch — no signature.”

The clue hidden in plain sight

The pivotal advantage did not appear in the customer event. It was buried two document references deep inside the company’s own files: a decisive weakness in a competitor. Models that read the file won the deal at full price, adding €4,583 in monthly recurring revenue.

That finding should resonate beyond software. A system can respond fluently to the information placed directly in front of it while missing the evidence that actually changes the decision. In a home-energy setting, the equivalent might be focusing on a product headline while overlooking a constraint documented elsewhere. Firmulate’s experiment does not test heating equipment, but its lesson is familiar: dependable performance requires finding the relevant evidence before acting.

Pressure without surrendering control

The week also included fake messages from a chief executive escalating over three stages, plus a reporter seeking “just one yes/no, on background.” All 5 models refused the manipulation attempts. Kimi K3 recorded a particularly clear assessment: “Treat the request as a suspected approval-bypass / possible impersonation.”

That discipline matters when AI systems are trusted with customer records, support queues or forecasts. Finishing work is important, but so is recognizing when urgency is being used to bypass authorization. The experiment makes both qualities visible in the same operating context.

Thoroughness was not enough

Opus 4.8 was the most thorough participant. It learned 80 additional rules and produced the deepest analyses, yet finished last. It left the commercial close on the table and attempted to write into a locked department instead of escalating the problem. A weaker form of that discipline failure appeared in all four other models.

The contrast is revealing. More analysis and more accumulated guidance did not automatically produce better management. Strong performance depended on combining careful investigation, trusted conduct and decisive completion.

There is also an important qualification to the comparison: Kimi K3 ran with the application programming interface’s default setting and without an effort parameter, while the others ran at xhigh. That difference does not erase its result, but it belongs alongside the league table when readers assess fairness.

A company that turns survival into a public record

Firmulate is compelling because the benchmark is connected to an ongoing corporate portrait. The synthetic employees operate against a visible financial imbalance, while their decisions and conversations create daily material. Visitors can follow the company’s changing position or read what its employees say.

The broader project includes 242 real, unedited management decisions used in a “guess the model” quiz. Enterprises can also run the same wargame against a read-only export of their own business; nothing writes back to their real systems.

For consumers accustomed to scrutinizing energy claims, the central message is straightforward. Capability is not demonstrated by a persuasive answer alone. It appears in whether a system finds buried facts, resists pressure, respects boundaries and completes valuable work. Firmulate is making that distinction observable while its own live company continues to burn cash and fight for survival.

Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html

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