Project Deal: our Claude-run marketplace experiment

A t Anthropic, we’re interested in how AI models could begin to affect commercial exchange. (You might recall Project Vend , where we had Claude run a small business from our office.)

Recently, economists have begun theorizing about a world in which AI models handle many or most transactions on humans’ behalf. We thought we’d run a new experiment—Project Deal—to learn more about this in practice.

Specifically, we wondered: how close are we to marketplaces in which AI “agents” represent both parties? Could they figure out what humans want and make deals they’d be happy with? And what would happen if there were different AI agents negotiating with each other—would stronger models gain the upper hand?

There was clearly a quantitative disadvantage to being represented by Haiku: these users got worse deals. But they didn’t seem to notice it. This has an uncomfortable implication: if “agent quality” gaps were to arise in real-world markets—and there is no reason to think they won’t—then people on the losing end might not realize they’re worse off. That said, our experiment wasn’t designed to dive deep into the dynamics at play here—we’ll need more research to know whether a fully agentic economy might see inequality taking root quietly.

Volunteers on board, we asked Claude to conduct an interview with each one, in a format much like our Anthropic Interviewer . This elicited a wealth of information: what our volunteers wanted to sell, how much they wanted to sell it for, what they were interested in buying, what they’d pay, and any other instructions they had for the negotiation or interaction style of their agents. These responses informed custom system prompts that we set for each person’s AI representative.

For one week, we created a classified marketplace for employees in our San Francisco office—like Craigslist, but with a twist: all of the deals were conducted by AI models acting on our employees’ behalf. In December 2025, Claude interviewed people about which of their personal belongings they might want to sell and what sorts of things they might be willing to buy. We incentivized participation by giving everyone’s agent $100 to spend. Then, our employees’ Claude agents made postings vying for each other’s attention. Negotiations commenced. Deals were made, closets decluttered. At the end of it all, people brought in and exchanged the actual, physical goods that were haggled over by their AI avatars—covering everything from a snowboard to a plastic bag full of ping-pong balls.

We were struck by how well Project Deal worked. Our AI agents struck 186 deals at a total transaction value of just over $4,000. To our surprise, participants were very enthusiastic about the experience—they even stated a willingness to pay for a similar service in the future.

But we also ran a parallel experiment (this one in secret). We tested how our participants would fare if we varied which Claude model represented them. We compared our then-frontier model, Claude Opus 4.5, to our smallest model, Claude Haiku 4.5. We found that agent quality does make a difference: people represented by “smarter” models got objectively better outcomes. Yet our post-experiment survey found that those with weaker models didn’t notice their disadvantage.