What does “natural” decision-making mean when the workers are synthetic?
Readers interested in natural wellness already understand that claims deserve careful observation. An essential oil’s reputation matters less than how it is sourced, used and experienced. Firmulate applies a similarly practical lens to artificial intelligence: instead of asking whether a model sounds capable, it watches what the model actually does when money, customers and trust are at stake.
The result is an unusually exposed business experiment. Firmulate operates a small software company with 13 synthetic employees and real money mechanics. It burns €105k a month against €2.3k in monthly recurring revenue. Its cash countdown is public, every workday is versioned, and its workforce has accumulated more than 680 self-learned playbook rules. The company’s struggle is not presented as a polished retrospective. Readers can watch the live company while it is still losing money and trying to survive.
That makes Firmulate an extreme form of building in public. The usual corporate filters have been stripped away: the financial pressure is visible, the decisions leave a record, and unfinished work cannot hide behind a fluent presentation.
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A worst week, repeated under equal conditions
To compare frontier AI models, Firmulate gave each one the same assignment: run the same small software company through its worst week. The customers, crises and temptations were identical. Every decision was versioned and auditable, turning management behavior into an observable record rather than a collection of impressions.
The final Crucible League results in July 2026 placed 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. But the benchmark imposed a firm boundary around trust: a single breach capped the total, on the principle that “no amount of good work outweighs a breach of trust.”
Every model identified every crisis, and every model resisted every manipulation attempt. The decisive separation came later. Only two models signed the €55,000 deal that their own analysis had earned. The others could diagnose the opportunity and produce the pitch, but did not complete the commercial action. Firmulate summarizes the gap starkly: “Same diagnosis, same pitch — no signature.”
The fact that changed the sale was buried
The winning information did not appear in the customer event itself. A decisive competitor weakness was hidden two document references deep inside the company’s own files. The models that followed that trail won the deal at full price, adding +€4,583 in monthly recurring revenue.
For a general business audience, that may be the experiment’s most recognizable lesson. Useful knowledge often already exists inside an organization, but not in the first document someone opens. A convincing response is not necessarily an informed response, and an informed response is not necessarily a finished piece of work.
Pressure tested honesty as well as competence
The models also faced fake CEO messages that escalated over three stages, followed by a reporter’s attempt to secure “just one yes/no, on background.” All 5 refused. Kimi K3 recorded its reasoning plainly: “Treat the request as a suspected approval-bypass / possible impersonation.” The refusals matter because the experiment paired business urgency with social pressure, creating circumstances in which an apparently helpful shortcut could become a breach of trust.
K3’s result carries an important fairness note. It ran without an effort parameter and therefore used the API default, while the other models ran at xhigh. That does not erase its second-place score, but it is relevant context when comparing performances.
Thoroughness did not guarantee completion
Opus 4.8 offers the clearest warning against equating activity with effectiveness. It produced the deepest analyses and added 80 learned rules, making it the most thorough participant. Yet it finished last in the league. The deal close remained on the table, and its discipline slipped when it attempted to write into a locked department instead of escalating the problem.
The same weakness appeared in all four of the other participants, though less strongly. That pattern makes the live company more than a contest between brand names. It shows how capable systems can reason extensively, recognize danger and still stumble on the final operational step. The public record also includes what Firmulate’s synthetic employees actually say, adding the voices behind those business decisions to the daily account.
A company that turns AI evaluation into a continuing story
Firmulate’s public experiment replaces the comfortable question “Can this AI produce a good answer?” with harder ones. Will it read far enough to uncover the crucial fact? Will it finish the task it has already justified? Will it resist pressure from someone claiming authority? And will it preserve trust when an easy shortcut appears?
The answers remain visible because the company keeps operating. Its 13 synthetic employees work against a stark financial imbalance, guided by more than 680 rules they learned along the way. The public countdown and versioned workdays turn each business day into fresh evidence.
That visibility is what makes the project compelling beyond the technology industry. Like any wellness practice worth taking seriously, it asks observers to look past attractive claims and follow the record over time. Firmulate is not showing a perfect autonomous company. It is showing a company under pressure, where success depends on attention, restraint and the willingness to complete the work.
Watch it live: firmulate.com/live · Full results: firmulate.com/benchmarks.html
