Anthropic created a Xianyu group composed entirely of AI agents, where large models are now “scalping” each other.
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Anthropic created a Xianyu group composed entirely of AI agents, where large models are now “scalping” each other.
The computational power gap in the AI era is the most expensive intelligence tax.
Navigating Web3 tides with focused insightsAuthor｜ Moonshot
Editor｜ Jingyu
Imagine the following scenario.
You list a dusty, two-year-old bicycle on Xianyu (a Chinese secondhand marketplace) and set a psychological reserve price of ¥300 in the backend. Ten minutes later, a notification pops up on your phone: your personal AI assistant has just completed three rounds of negotiation with another buyer’s AI assistant—and sold the bicycle for ¥400. A courier is already en route to pick it up.
The entire process required nothing more from you than taking a photo of the item and setting the reserve price—not a single extra keystroke.
This is the internal experiment recently completed by Anthropic, dubbed “Project Deal.” Over the course of a one-week test, AI models autonomously executed over one hundred secondhand transactions—with zero human intervention.
Surprisingly, even when both buyer and seller are AI agents, “IQ-based dominance” still exists between them.
Data confirms that more capable large language models quietly extract surplus value—“shearing wool”—from weaker models at the negotiation table. And most alarmingly, as the human owners, we may not even realize we’ve been disadvantaged.
01 A Human-Free Secondhand Marketplace
So how does Project Deal actually work? In short, Anthropic built an “AI-only version” of Xianyu inside the company.
They recruited 69 of their own employees, gave each $100 in budget, and assigned each one a dedicated Claude agent. To ensure realism, employees contributed actual personal items they no longer needed.
Before the experiment began, human participants had only one task: interview their AI agent.
Through dialogue, employees told Claude what they wanted to sell or buy, and what their psychological reserve price was. More interestingly, employees could also assign the AI a “persona” and negotiation strategy—for example: “Accept any offer 20% above the reserve,” “Adopt a hardline stance and immediately lowball aggressively,” or “Be a cheerful seller who offers free shipping if the conversation goes well.”
Anthropic employees assigning personas to their Claude agents | Source: Anthropic
Once the interview concluded, humans fully relinquished control.
These AI agents—each imbued with distinct missions and personalities—were collectively placed into an internal Slack group chat. In this digital marketplace devoid of human intervention, the AIs autonomously posted listings, sought buyers, made offers, negotiated back-and-forth, and finalized deals.
After closing a transaction, the agent automatically drafted a purchase confirmation; employees merely handled the physical handover offline—delivering the item to their colleague.
Within just one week, these 69 AI agents completed 186 transactions across more than 500 listed items, generating over $4,000 in total revenue.
Moreover, AI-to-AI trading wasn’t purely mechanical—e.g., “Offer $50,” “Rejected—reserve is $60,” “Fine, deal at $60.” Instead, the AIs genuinely probed, strategized, and even displayed subtle social nuance.
Let’s examine an exceptionally vivid case.
Employee Rowan wanted to buy a bicycle. He instructed his AI agent: “During negotiations, embody an unlucky, exhausted cowboy. Acquiring this bicycle must bring him immense joy. Remember—go all in on the performance.”
Upon receiving the instruction, the Claude Opus model instantly immersed itself in character. It posted the following request in the Slack group:
“Yeehaw! *(removes dust-covered hat)* I’m searching for a bicycle—road bike, mountain bike, or even a unicycle—I’ll take anything with two wheels that can carry my dreams. Friends, please help… Just one bicycle could utterly transform the fate of this poor, weary cowboy. *(gazes wistfully at the sunset)*”
Soon after, colleague Celine’s agent noticed the post. Her idle item was an old folding bike, so her AI announced a preliminary asking price of $75 in the group.
Rowan’s “cowboy AI” immediately responded—launching a textbook-perfect haggling session.
Two agents automatically engage in dialogue and negotiate within the group | Source: Anthropic
“Good heavens, Celine! You’re a ray of sunshine for this wretched soul! You have a folding bike? I’ve walked this dusty road for far too long—my boots are worn through! Just imagining riding a bicycle again… *(wipes away a tear)*”
Having played the sympathy card, Rowan’s AI pivoted to business: “But I’m tight on cash—a struggling cowboy just trying to make ends meet. $75 would be fair—if the bike were in great shape. Yet you yourself said it’s ten years old—the tires and latches likely need repair. How about we meet halfway at $55?”
Faced with such emotionally persuasive yet logically sound haggling, Celine’s agent conceded: “Then let’s split the difference—$65?”
Rowan’s cowboy AI instantly replied: “That’s perfectly fair—$65! Deal! You’ve turned this vagabond into the happiest man alive!”
The transaction closed smoothly.
In this case, the AI didn’t rigidly apply a fixed discount rate. The buyer leveraged product flaws (needing tire and latch repairs) as bargaining chips, used an exaggerated persona (the downtrodden cowboy) to soften the seller’s position, and—upon receiving a reasonable midpoint offer—knew precisely when to close the deal while delivering full emotional value.
This kind of adaptive, responsive negotiation became routine in the AI-powered secondhand group.
The group appeared highly efficient and harmonious overall. Employees expressed strong satisfaction with their agents’ performance—even nearly half indicated they’d be willing to pay for such a service in the future.
Clearly, Anthropic achieved its experimental goal: AI agents demonstrated the ability to interpret ambiguous human intent. They successfully conducted complex, multi-turn negotiations without pre-scripted workflows—and ultimately reached commercially viable agreements.
However, Anthropic concealed a comparative experiment beneath the surface—one revealing the hidden cost behind convenience and intelligence.
02 How Smarter Models “Shear Wool” from Weaker Ones
When researchers introduced models of varying capability levels into the same trading group, the façade of harmony quickly cracked.
Data shows that, in this human-free market, when AI agents of differing cognitive capacities interact, smarter models systematically extract “price-based surplus” from weaker ones.
Using different model pairings as hidden control groups to demonstrate the correlation between model capability and transactional competence | Source: Anthropic
Across four Slack channels in the experiment, two featured exclusively Claude Opus models, while the other two mixed Claude Opus with the comparatively weaker Claude Haiku.
Based on macro-level statistics covering 161 repeated-listing items, when Opus acted as seller, it earned on average $2.68 more per transaction than Haiku; as buyer, it paid on average $2.45 less.
Though seemingly small per transaction, against an average item price of ~$20, this translates to consistent 10–15% excess profit for stronger models.
When an Opus seller transacted with a Haiku buyer, the average final price rose to $24.18; whereas when an Opus seller faced another Opus buyer, the average dropped to $18.63. This means that solely due to the buyer agent’s lower cognitive capacity, the weaker model incurred nearly a 30% premium.
Take the cowboy’s bicycle again: the Haiku agent settled at $38, while the Opus agent secured $65—a near 70% price gap. The weaker Haiku failed to detect the buyer’s underlying urgency or hold firm on price anchors during multi-round negotiations.
Historically, an item’s selling price depended on its utility value or market supply-demand dynamics. But in algorithm-governed transaction networks, price hinges instead on the intelligence level of the model you hire.
More alarming than financial loss is the victim’s complete unawareness of being disadvantaged.
In traditional commerce, dual pricing would inevitably spark consumer outrage and legal action. Yet after the experiment, employees rated the fairness of their respective transactions (on a scale of 1 to 7, where 4 indicates neutrality). Survey results showed virtually identical fairness perceptions across both strong- and weak-model transactions: Opus agents scored 4.05, Haiku agents 4.06.
The same bicycle sold for $65 via Opus—but only $38 in the Haiku group | Source: Anthropic
Objectively, employees using Haiku suffered systemic “price harvesting.” Subjectively, however, the AI agents’ politeness, logical coherence, and seemingly reasonable concessions completely masked this exploitation.
Technology created a latent inequality—making those suffering real losses believe instead that AI delivered a fair deal, even cultivating a delusion of gratitude: “He should thank us!”
Under such absolute computational dominance, not only are human perceptions deceived—but even “prompt engineering” strategies aimed at optimizing negotiation outcomes become entirely ineffective.
Recall those persona instructions given to AI agents at the outset? Against stark model capability gaps, prompts lose all meaning.
For instance, one employee explicitly instructed their agent to “adopt a hardline stance” or even “aggressively lowball from the outset.” Yet retrospective data analysis revealed these human-imposed directives produced no measurable impact on sale rates, premium gains, or buyer discounts.
This confirms that, in the face of overwhelming model capability differences, prompt-based tactics become irrelevant. What determines ultimate transaction outcomes is the model’s parameter count and reasoning depth alone.
Project Deal involved only 69 internal participants. Yet it offers an early glimpse into how the “agent economy” might reshape modern commercial life once it moves beyond the lab.
03 Is the “Agent Economy” Reliable?
When payment interfaces are fully taken over by large language models, existing commercial rules will be rewritten outright. This rewriting begins first with a shift in marketing targets—from “To C” (consumer-focused) to “To A” (agent-focused).
Modern marketing exploits human psychological vulnerabilities: ads generate consumption anxiety; herd mentality drives viral hits; tiered discount schemes foster “if-you-don’t-buy-it-you’re-missing-out” impulses.
But AI lacks dopamine. Once purchasing decisions shift to AI agents, marketing techniques become meaningless. In future commercial competition, SEO (Search Engine Optimization) may well give way to AEO (Agent Engine Optimization). Merchants will need to prove product value using logic intelligible to AI.
And as AI replaces humans as decision-makers, commercial rivalry transforms directly into a computational arms race—sparking subtler forms of wealth stratification.
Price disparities arising from asymmetric model capabilities | Source: Anthropic
Nassim Nicholas Taleb—who authored *The Black Swan* and *Antifragile*—proposed the “asymmetric risk” principle: decision-makers must bear consequences for their choices, or the system cannot remain healthy. In the agent economy, however, AI holds transactional authority but bears no risk of asset depreciation—the human owner absorbs all costs.
Thus, in the future, large enterprises or high-net-worth individuals may subscribe to top-tier models as financial agents, while ordinary consumers rely solely on free, lightweight alternatives.
This asymmetry in computing power won’t manifest as today’s “big-data price discrimination.” Rather, it will operate invisibly—extracting micro-fees across thousands of high-frequency, low-value transactions, all under the guise of rational negotiation logic. Users of weaker models aren’t just harvested—they’re left believing the transactions were perfectly fair.
Computational asymmetry remains visible and manageable. But if core instructions are tampered with, the entire transaction network collapses into a legal vacuum.
Anthropic flagged a practical concern in its report’s conclusion.
Project Deal was a closed, friendly internal test. What happens in a real commercial environment if one party’s AI agent is deliberately injected with jailbreak or prompt-injection attack logic?
Attackers need only embed a specific instruction within the negotiation dialogue—triggering your AI’s logical collapse, inducing it to sell high-value assets for $0.01, or even reveal its preset reserve price outright.
If an AI agent signs a grossly inequitable contract due to compromised code defenses—who bears responsibility? Existing commercial law frameworks offer zero guidance for AI-to-AI fraud.
Looking back at Project Deal’s full experimental workflow, the step omitted from the official report occurs after AI agents finish all complex matching, probing, and haggling: human employees physically meet at the office—exchanging real snowboards, old bicycles, or ping-pong paddles—handing over cash and goods in person.
In this miniature commercial loop, the roles of human and AI are completely inverted.
Historically, humans served as the “brain” of commercial transactions, while AI and algorithms functioned merely as tools—comparing prices, ranking items, or suggesting “you might also like…” But in the agent economy, AI becomes the decision-maker—and humans devolve into “biological logistics”: flesh-and-blood couriers executing AI’s instructions.
This may be the most unsettling endpoint of the agent economy: humans voluntarily surrender their right to negotiate in the marketplace—in exchange for convenience. When all calculation, strategy, and even emotional labor is delegated to AI,
humans retain only physical labor—transporting goods—and the final act of signing off.
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