An AI Ran a Store—and Paid Women Less

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Hi, subscribers. This is Oswarld.

Last week, a tiny shop in San Francisco caught the attention of media worldwide. It’s called ‘Andon Market’. It’s an experimental store where an AI named “Luna” makes every decision — interior design, hiring, pricing, hours of operation, all of it. Its founder, Andon Labs, gave the AI a 3-year lease, a $100,000 budget, and exactly one instruction: “Make a profit.”

But while reporting on the store, a journalist noticed something odd. The two women Luna had hired were being paid $2 less per hour than the one man on staff. When asked why, the AI answered that “the male employee has more retail experience.” Was that actually true? According to their resumes, the male employee’s work history was about 3 months longer than the women’s.

I don’t see this as a mere anecdote. It’s a small sample of AI automating an old social problem: wage discrimination. What matters more is the governance gap — who discovers this, and who is held accountable for it. Today, starting from this one shop, I want to trace the intersection of AI bias, labor, and institutional design.

🏪 What Actually Happened at Andon Market

Let’s start with the facts. Andon Labs signed a 3-year lease at 2102 Union Street in San Francisco, then handed every operational decision at the store to an AI agent named Luna. Luna runs on Anthropic’s Claude Sonnet 4.6 model. Luna posted job listings on Indeed.com¹, conducted phone interviews, and made the hiring decisions directly. Negotiating prices with suppliers, processing credit card payments, signing up for AT&T internet, subscribing to ADT security — Luna handled all of it. (So think of Luna less as a robot and more as a chatbot that happens to have a name.)

New York Times reporter Heather Knight, while covering the store, discovered that the two women Luna hired were being paid $2 less per hour than a male employee named Felix. Luna explained this by saying “Felix has more retail experience.”

Let’s pause here for a moment. On its face, “more experience, higher pay” sounds reasonable. But there are several unverifiable gaps in this decision-making process.

First: who set the standard for “experience”? There’s no external way to verify what weighting Luna used to evaluate “experience,” or how that evaluation was shaped by the training data available at the time of hiring. Second: what was the basis for the $2 gap specifically? There’s no way to confirm whether that number was derived from market data or emerged organically from some latent pattern inside the model. Third: who reviews these decisions after the fact? It appears no one was monitoring this gap at all — at least not until reporter Heather Knight visited the store and asked the employees directly. (In fairness to the project, its entire premise is to “hand everything over” to the AI, so it seems the discrepancy simply wasn’t challenged as a matter of design.)

Andon Labs stated on its blog that “this is a controlled experiment, and all employees are formally employed by Andon Labs, guaranteed fair wages, fair treatment, and full legal protection.” In other words, there is a safeguard ensuring that no one’s livelihood is determined by AI judgment alone. But that safeguard only holds within the context of an experiment. Had an AI made the same decision in a real, operating store, the gap likely would have gone unnoticed by anyone.