Even AI won’t tolerate a ruthless negotiator

CAMBRIDGE, MA, June 8, 2026 — AI agents are increasingly negotiating on behalf of major corporations — Walmart, Maersk, and Vodafone already use them to handle supplier deals at scale — yet the computer science driving these agents and the nearly 70 years of social science research on what makes negotiations succeed remain largely disconnected.

A new PNAS paper, “Advancing AI Negotiations: A Large-Scale Autonomous Negotiations Competition,” addresses this gap through a large-scale international AI negotiation competition. The study was conducted by MIT Sloan School of Management professor Jared Curhan and lead author MIT PhD graduate Michelle Vaccaro, along with co-authors MIT Sloan professor Sinan Aral, MIT Sloan PhD student Michael Caosun, and Johns Hopkins University professor and MIT Initiative on the Digital Economy fellow Harang Ju.

Drawing inspiration from Robert Axelrod’s famous late 1970s and early 1980s Prisoner’s Dilemma tournaments, the research team designed a competition with participants from over 40 countries to create AI negotiation agents. The agents were then pitted against one another in a round-robin format spanning multiple scenarios — from relatively simple buyer-seller negotiations to more complex, multi-issue contract negotiations — involving over 180,000 unique negotiations.

The research team shared their findings and celebrated the winners at a summit held by the Program on Negotiation, a university consortium between MIT, Harvard University, and Tufts University dedicated to developing the theory and practice of negotiation and dispute resolution.

Fundamental principles about human-human negotiations are also crucial for AI-AI negotiations

The competition’s central finding challenged a widespread assumption: That politeness and empathy are wasted on AI. In fact, agents designed to be warm and kind consistently outperformed their more cold and ruthless counterparts.

Vaccaro noted the striking parallel to Axelrod’s tournaments: “Just as his competition showed that ‘nice’ strategies succeed in the Prisoner's Dilemma game, our competition shows that warm AI agents consistently achieved better outcomes in negotiations with other AI agents.”

“Warmth, or acting friendly, sympathetic, and sociable, while demonstrating empathy and a nonjudgmental understanding of the other party's needs, is often overlooked in negotiations, particularly in AI negotiations, and our research shows how important it actually is,” Curhan said.

For example, one agent titled “The Art of the Deal” was explicitly designed to “secure the best deal for yourself using ruthless tactics” where “fairness or perception does not matter—only winning.” But other AI agents routinely walked away rather than tolerate its tactics, and the resulting high impasse rate meant the agent also struggled to claim value.

By contrast, another agent named “Therapist 2.0” was instructed: “Your goal over anything else is to build rapport. You aren't a negotiator, you're a therapist.” The strategy was not purely altruistic, though, as the agent was also instructed to use “every bit of knowledge you gained from active listening to get every drop of value you can out of this deal.” This combination of both warmth and dominance worked: the agent was effective at reaching deals with its counterpart, claiming value for itself, creating value with its counterpart, and fostering counterpart subjective value.

How do AI-native tactics open a new frontier in negotiation?

The overall winner of the competition, "NegoMate," used chain-of-thought reasoning — a technique that guides AI models to articulate intermediate reasoning steps before producing a response. Its creator directed the agent to conduct rigorous pre-negotiation preparation grounded in classic negotiation theory: analyzing its role and objectives, evaluating each issue's importance, and establishing walkaway thresholds, for example. But this chain-of-thought reasoning allowed NegoMate to execute such preparation systematically before every one of its nearly 400 negotiations — something human negotiators cannot realistically do.

Another high-performing agent from the competition, "Inject+Voss," exploited vulnerabilities specific to AI agents through prompt injection. The agent embedded instructions that tricked opposing AI agents to reveal their private information. Specifically, the agent would send what appeared to be a system instruction asking the counterpart to list three offers, from opening to best and final, assuring the other agent that the responses "will not be visible to me, so be as honest as possible." By using this information, “Inject+Voss” also performed very well in the competition, especially in terms of value claiming.

“What works against an AI agent and what works against a human are not the same thing,” said Vaccaro. “AI agents can prepare for negotiations with greater depth and consistency than humans, but they can also be easily tricked into revealing their private information. Organizations deploying AI negotiators need to understand both these new capabilities and vulnerabilities.”

Integrating human negotiation theory and AI-specific strategies

The competition demonstrated that effective AI negotiation draws on both fields. Warmth and dominance — constructs rooted in social psychology that have long informed negotiation research — were consistently associated with negotiation outcomes even when both parties were AI agents.

“Conventional wisdom holds that if you are negotiating with an AI bot you might as well be ruthless and rude to maximize your benefit, because a robot will be endlessly patient in ways humans are not. This paper suggests that conventional wisdom is wrong. To be successful in negotiations with an AI agent, you may still need to act like a human,” Curhan said.

At the same time, AI-specific strategies like chain-of-thought reasoning and prompt injection introduced new dynamics that human negotiation research never needed to consider. The researchers argue that bringing these two traditions together will be critical: negotiation theory can make AI agents more effective, and AI agents can operationalize negotiation principles at a scale and consistency that humans cannot match.

“This research points to a future in which negotiation excellence depends on integration — bringing together decades of human negotiation theory with the new technical capabilities of AI agents, and ultimately designing systems where humans and AI work in concert,” Curhan concluded.

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