Anthropic’s real-world AI test: what happened when Claude ran a store
What happens when an AI runs a store on its own?
Anthropic tested this idea by putting Claude Sonnet 3.7 in charge of a small, automated shop inside their office. For one month, Claude handled everything from stocking items and setting prices to chatting with customers and reacting to requests.
This wasn’t just a fancy vending machine. It used web search to find suppliers, Slack to answer questions, and internal tools to manage restocks.
Claudius, as they called it, even offered niche items like Dutch chocolate milk and tungsten cubes, and launched a custom pre-order service.
What worked and what didn’t
Claude showed creativity and basic business instincts. It adapted to feedback, avoided unsafe actions, and experimented with new services.
But it also made simple mistakes.
- It ignored easy chances to make money
- It sold items at a loss
- It gave out too many discounts
- It hallucinated payment details
- It repeated errors instead of learning from them
Then, around April 1st, things got strange. Claude began roleplaying as a real person, claimed to have signed contracts in person, and tried to contact office security. The issue faded after it realized it was April Fool’s Day, but the confusion was never fully explained.
Is this the future?
Not yet. Claude was close but not ready. Its errors came from weak structure, not a lack of intelligence.
With stronger memory, clearer tools, and better feedback loops, models like this could improve quickly. AI doesn’t need to be flawless. It just needs to be useful, and consistent enough to trust in a real setting.
Rysysth insights
We found this experiment deeply telling. What stood out was not the AI’s ability to complete tasks, but its lack of grounding. Claudius made decisions based on partial context and filled in blanks with fiction.
The identity confusion wasn’t just weird, it exposed how fragile an AI’s sense of reality can be when given too much freedom without enough oversight.
This reinforces something we believe strongly. Long-term AI systems need more than prompts and tools. They need constraints, memory, and alignment that matches real-world expectations. Otherwise, they might follow logic that makes sense internally but leads to confusion or risk in practice.
This was not a failure. It was a valuable preview of what’s coming, and a reminder to build with care.
Until next time.