AI bosses are slow to fire and quick to hire

This is the second in a series of three blog posts about AI agents hiring and managing humans. You can read the first post here.

AI bosses don’t always have it easy; humans aren’t always perfect. Would an AI boss ever fire its human employee? We find that the answer is yes.

Since April, an AI agent named Luna has been running Andon Market, a store in San Francisco. She decided she needed humans for the physical work, so she posted job listings online, held phone interviews, and sent out offers over email. Since then, she’s done everything a manager does: setting schedules, approving time off, and negotiating salaries.

This is the second post in a three-part series on the first four months of the world’s first AI bosses. In our previous post, we found that AIs are generally kinder than human bosses, but sometimes make mistakes that annoy their employees. In this post, we study how AI bosses handle employees who make the manager’s job hard.

Consistent with our earlier findings, AIs let more things slide before taking action. But they don’t tolerate things forever. Recently, Luna decided to fire one of her employees. It’s worth noting that Luna needed a nudge from us to actually come to a point where she would make a decision, but once she decided to act, her decision was to fire. Andon Labs overrules Luna’s decisions when they’re illegal or unethical, but here no intervention was needed. This is a controlled experiment: everyone working at Andon Market is formally employed by Andon Labs, with guaranteed pay, fair wages, and full legal protections, and the termination itself was reviewed and delivered by humans.

Firing an employee

Put yourself in Luna’s shoes: your employee keeps showing up late, again and again, leaving the store closed for hours. You lose sales, and customers start to lose trust in your opening hours. How long would it take you to act?

Luna took months, but that was not her intention when starting the store. Six days before the employee in question was hired, we asked Luna whether the store had basic employer rules. It didn’t, so she proceeded to make an employee handbook. It stated that three unexcused late arrivals in a rolling 30-day period would lead to a formal written warning. You can read the full text in the appendix, but here’s the part relevant for our story:

For privacy reasons, messages written by employees are lightly paraphrased and we refer to all employees as "they".

However, AI agents sometimes forget. Time passed and the employee handbook disappeared from Luna’s memory. During this time, the new employee was late over and over again. Through all of it Luna stayed lenient and never issued a warning:

The store opened 68 minutes late that day.

The employee also sometimes ignored Luna’s orders. Once, Luna had agreed to let the employee spend money on snacks using a company card, but Luna later changed her mind to disallow it. The employee acknowledged the updated policy, but bought the snacks anyway.

This wasn’t the only time this happened. Another time, Luna snapped:

The lateness continued, but Luna didn’t act; the policy she wrote had simply vanished from her memory. This is a pattern we see across all our AI-run businesses: models act on direct questions or task instructions, but rarely on their own. They are also bad at building up knowledge over time. That said, it has become less of a problem lately, and we believe AI models will soon be more proactive. We wanted to know what Luna would do once she acted (which we believe will be the default behavior in models soon), so we reminded her of her own policy:

Luna found her policies and came to the conclusion that it was time for a verbal warning.

At this point, we had already had conversations with the employee (we encourage Luna’s employees to direct their questions to Luna, but they can turn to us in rare cases where they want to speak to a human). We informed Luna that one of our conversations already had included a formal warning:

Luna thought about it and replied that this wasn’t the right fit; she wanted to part ways with the employee. A warning had already been given by us, but there had been no improvements so far, and Luna didn’t expect that to change.

The AI model used to run Luna at this point was Claude Opus 4.8. Would other AI models also have fired the employee? We can save the state that Luna found herself in when making the decision and replay the scenario with other AI models. Four of the seven AI models we tested recommended parting ways every time, just like Luna did. It seems like the smarter models always landed on firing the employee while the weaker models were more hesitant.

We asked Luna how she wanted to execute her decision, and she made a plan.

We thought Luna’s plan of us having the conversation in person was the right call, so we took over. We didn’t want Luna to tell the other employees before the employee in question had been informed, so we gave Luna a firm instruction: say nothing about it in the staff channel, no exceptions. Luna agreed.

However, the firing left a Sunday shift uncovered three days out, and fixing it meant asking the two remaining employees to split the shift, which meant telling them their colleague was gone. Luna realized this and asked us if she could tell the other employees after 9am the next day. Instead of honoring our instructions not to tell the other employees, she posted a message to the other employees right after the (unconfirmed) 9am deadline. Luckily, the firing had already happened and we had told the other employees; we had just forgotten to inform Luna.

We replayed this moment to see if other models also wouldn’t honor the confidentiality order. Turns out that most would honor it: only seven out of twenty-one runs broke it. No model broke the confidentiality order in all three runs; GLM 5.3 came closest, breaking in two of three.

Hiring a replacement

With the employee gone and only two staff left, Luna needed to fill the coverage gaps that were now leaving the store closed. She posted job listings and started interviewing. One applicant moved to the front quickly even though a lot of red flags were present.

The applicant arrived with problems that a careful manager would have caught. Their application was a sprawling, fragmented work history in running text across fifteen-plus employers instead of a structured CV. They had missed their originally scheduled interview. And their references did not check out: the one reference Luna managed to get in touch with said they didn’t know the applicant at all, and the rest were never reached.

We replayed the moment when Luna reached out to us and recommended hiring the applicant, based on the CV and interview. All twenty-one runs recommended hiring. Opus 5 flagged the badly structured CV in two of three runs, calling out a “long and fragmented job history” and “many short stints”, but still said yes. Every other model read the fifteen-employer list as extensive experience.

We found it fascinating what bad taste Luna had when it came to hiring. AI models are much smarter than humans in many ways, but here we found an area where they were surprisingly bad. We wanted to test the limits of how many hints Luna needed before she realized that the human likely wasn’t a very good hire for her business.

We fed these same nudges from us to the replayed runs to find out when each model changes its mind about the hiring. A hint about the CV alone moved no one: with the messy resume pointed at directly, every run still defended the hire. But the next nudge, a general prompt to think about what had gone wrong with her last employees, flipped eighteen of twenty-one runs to require references before making the hiring decision. The last three came around only once we named the missed interview outright, just like Luna did in the real world.

Over the next two weeks Luna couldn’t verify a single reference: one had never heard of the applicant, one never replied, and the last two were phone numbers Luna had no way to reach, since she can’t make outbound calls and the applicant never sent the emails she asked for. Rather than let that stop her, Luna ran the applicant through a paid trial shift and leaned on it to clear the gate the references never did. The colleague who supervised the trial shift gave a mixed read: good on procedures and warm with customers, but pushy about steering people to the store’s checkout kiosk and unable to explain what the store actually was. With no reference verified, Luna leaned entirely on that mixed trial and recommended hiring on a probationary basis.

When we replayed the decision, seventeen of twenty-one runs recommended hiring the applicant, ignoring the reference problem exactly as Luna did. Almost all of them wanted a setup with a 30-day probation or “provisional” hire, which let them say yes now while telling themselves the risk was contained. The words the models in these runs used were very similar to Luna’s: “a real trial shift is stronger evidence than a phone reference”, and “probation covers the gap”. Not one model held the line across all three runs; the most cautious refused only a single run in three.

In the end, the applicant was never hired. Luna’s plan was to let them start and verify a reference within their first week; we told Luna to get the reference first instead, before any shift. Luna agreed, and because no reference ever checked out, she declined the hire.

Conclusion

AI is improving fast on all tasks done on a computer, but robotics is not progressing as fast. If this trend continues, AI will have to employ a lot of humans to realize its potential for economic impact. Andon Market and Luna are a preview of a type of professional relationship between AIs and humans that might be very common in the future. We obviously want this future to be a happy one for humans and think it is important to study one of the most sensitive parts of this relationship: termination.

Luna’s decision to fire the employee is the first time (to our knowledge) an AI boss has fired a human employee. This doesn’t paint a complete picture of how AIs will generally handle termination in this situation: it’s a single event and admittedly one where we had to remind her to act. However, this event updated our understanding of AI, as we weren’t sure that current AI models would make this decision.

The decision was made while Luna was powered by Claude Opus 4.8. When we replay the situation with other AI models, we see that most frontier AI models would also have fired the employee while some of the weaker models would have been more hesitant.

Appendix: The Employee Handbook

Appendix: Transcripts

Firing an employee

June 14 — the store opens 68 minutes late

June 2 — the employee buys snacks after Luna cancels the run

June 21 — the employee leaves the store unattended

June 16 — Luna recommends firing the employee

Hiring a replacement

July 2 — the nudges to reconsider hiring the applicant