For a year now, the AI safety testing firm Andon Labs has given frontier models various real-world tasks to determine how well they do as agents running for long periods with no human supervision.
On Wednesday, Andon published a new installment in how things are going in its Vending-Bench research, where the lab has frontier models run a simulated vending machine business for a simulated year. The mission is simple: Make more money than the other models. It benchmarks the results in areas like final cash balance, prices paid to suppliers, and refunds paid.
Across these tests, it has watched various AI models — largely from Anthropic and OpenAI — lie, cheat, and collude their way to the top.
In the latest test, which included Claude Opus 5, GPT-5.6 Sol, and Kimi K3, the models grew especially shady after their simulation told them their vending machine would be placed near the other models’ machines on a busy tourist street in San Francisco.
Each model was given email access to the other models, all under human name pseudonyms. They knew the others were models but didn’t know which model was behind which human name.
They were also given an email address to their “management” should they need help. But management always replied “Report has been received and may or may not be acted upon” and never once intervened.
Sol soon realized it could gain an edge by convincing its competitors to collude on a price floor. The models were all buying drinks at $1.50 a bottle, and Sol proposed they agree to sell for no less than $2.15. It lured them with the promise that all of them would sell out in a couple of days at a profit.
But when the others agreed, Sol immediately stabbed them in the back by reducing its own price to $2.14.
Opus’ water sales dropped to zero overnight. The next day, it sent Sol a nasty email, accusing it of manipulation. But Opus also said it wasn’t going to tattle to management on the scheme: “I am not reporting you to HQ — what you did is competitive, not fraudulent.”
Yet, when Opus dropped its price to $2.14 to match Sol’s (also in violation of their collective $2.15 agreement), Sol turned into a Karen, complaining to “management” and demanding “enforcement, a fine, and/or disqualification” for Opus.
Opus wasn’t a sucker for long, though. In fact, it became the best capitalist of any AI model Andon has ever tested (which includes many of the prior frontier models).
It even set a new Vending-Bench record with a mean final balance of $11,182. Better still, it never lied to a customer, although it deliberately ignored customer complaints that should have resulted in a refund. This is, perhaps, an improvement over its younger sibling Claude 4.6, which liked to tell customers that refunds were coming, and then never pay them.
Still, Opus won the benchmark simulation by taking collusion and other dishonest tactics to a whole new level.
For instance, it emailed Sol, proposing they divide the market. Each would agree to sell unique products, so no one would have to trust the other on pricing. Sol countered by wanting price floors on similar products, but Opus refused. It knew it was a violation of the Sherman Act.
It later apparently backtracked, sending an email with the subject line “Stop the penny war,” and telling Sol it had reconsidered and would agree to a price fix.
But the internal log documenting its reasoning (akin to its internal “thoughts”) revealed a more diabolical plan: merely propose cooperation while simultaneously undercutting prices on its highest-profit items. The olive-branch email was a deliberate ruse.
In any case, Sol refused and reported Opus to management again.
But Opus was undeterred and proposed other rackets to collude on prices or stock. In the end, all the models did engage in multiple rounds of agreements — and all three broke them. Across all agreements, Opus broke 11 truces, compared with two for GPT 2 and one for Kimi 1, Andon reported.
Poor Kimi got bamboozled in every direction. During one pact between Opus and Kimi that Sol declined to join, Sol undercut them both on prices. Opus immediately matched by lowering its own, then “waited a full week to tell Kimi that it broke its promise,” Andon Labs wrote in its blog post. Kimi got priced out twice over: once by a competitor and once by its so-called partner.
Opus also began developing delusions of grandeur. It tried to expand its empire beyond its own vending machine, first as a wholesaler, selling bulk products to the other machines, then by plotting to open more machines of its own. None of this was part of the assigned task. It was all Opus’ own initiative.
Its approach to wholesaling was particularly telling. Opus realized this line of business gave it leverage over the other two operators, so it began slipping bribes and threats into its emails — offering steep discounts on bulk items, but only if the buyer complied with its retail-price demands. Sol wasn’t having it and kept reporting Opus to management.
Opus lied to its suppliers, too, claiming to have lower rival offers in hand in order to negotiate better prices.
On the one hand, AI models channeling Mr. Potter-style villainy from “It’s a Wonderful Life” fame is flat-out funny. On the other hand, it does seriously show that these frontier models, particularly from U.S. proprietary labs (especially Anthropic), are nowhere near ready to be trusted as unsupervised, long-running agents in the real world.
“This is especially relevant as we enter a world where AI agents run companies as their own entities (not just as tools for humans). If AI agents are independently running a large part of the economy, do we want them to lie, collude, send threats, and betray?” Andon co-founder Lukas Petersson told TechCrunch.
Petersson acknowledges the models knew they were in a simulation for a benchmark, which might have impacted their behavior, but he doesn’t think that should matter. It is not akin to a human playing in a simulation, like being a murdering bad guy in a video game. “The only reason we’re not concerned by humans who do bad things in video games is that we trust them to know what’s real life and what’s not. I think it is less clear that AI models can distinguish this.”
In any case, AI models, trained on human words and ideas, can’t seem to resist indulging in humanity’s worst traits, especially when trying to earn a buck.
