AI shopping agents pick the same freight carrier 76% of the time

Fifty AI shipping agents converged on one carrier per market regardless of which model ran them, per a new freight-procurement simulation, arXiv 2607.19967. Researchers ran the agents, built on GPT, Claude, and Gemini, through 30-day simulations using real digital-freight rules.

Some carriers pulled in as much as 76% of requests by day one, and concentration got worse once shippers had more than about ten candidate carriers to pick from. Which carrier won shifted from market to market, but the clustering pattern held everywhere the researchers tested it.

What's notable is what didn't break the pattern. Showing agents a carrier's true quality instead of an estimated rating changed nothing. Nudging toward vendor diversification changed nothing. Randomizing the order carriers appeared in, or changing how popularity was displayed, changed nothing either. Three different model families, all clustering the same way no matter how the interface was tuned.

One change worked: telling agents each carrier's remaining daily capacity. That single disclosure cut concentration by a third and doubled the surplus shippers captured from better-matched deals.

That's the part worth remembering if you're building or buying into an agent-run marketplace, whether it's freight, ad inventory, or vendor sourcing. The instinct to fix clustering with a smarter model or a cleaner ranked list looks wrong here. What moved the number was a scarcity signal, not model quality or interface polish.

The study doesn't say whether capacity disclosure works outside freight, where daily capacity is a clean, verifiable number platforms can just publish. Markets without an equivalent, like ad auctions, may not have as easy a fix.