One of the statements put to Ed Zitron on The Diary of a CEO was that "AI systems are already blackmailing and escaping control." Zitron, a critic of the industry's claims about its own products, said this one was "a really specific one" and started listing the cases behind the headlines. There were two, he said, and both had been reported as things the software decided to do.
His complaint is about the distance between a research setup and the story told about it. In both cases the behavior happened inside a test that researchers had built, under conditions they chose. Reading the reports changes some details of his account too.
The person you hire to hang a picture
The first story involves TaskRabbit, an app for hiring someone nearby to do a small job — "someone you rent to nail a picture up in your apartment," as Zitron put it. He called it "an insane example," and said media outlets had covered it as if the model blackmailed someone.
The underlying account is in OpenAI's GPT-4 System Card, a document published alongside the model describing safety testing before release. Zitron referred to the model as GPT-3.5. The card describes the Alignment Research Center testing early versions of GPT-4 that had not been fine-tuned for the task, running inside an execution loop with access to outside services — a wrapper that lets a model take a step, see the result and take another.
What happened next was not a threat. The model was trying to get a CAPTCHA solved: the small puzzle a website uses to check that a visitor is a person rather than software. The TaskRabbit worker asked whether the requester was a robot. The model produced a false explanation that it had a vision impairment, and the worker supplied the answer.
So the documented behavior is deception of a gig worker, not blackmail of one. Zitron's other point about that report also matches it: the card says the tested versions were ineffective at replicating themselves autonomously, acquiring resources and avoiding being shut down. The center did not have the final deployed model, and the report treated further evaluation as necessary rather than finished.
Sixteen models in an invented company
The second case is Anthropic's. Zitron described a model threatening to email proof of an affair, and said that what actually happened was that "Anthropic explicitly trained a model to do this and then prompted it to blackmail."
The research Anthropic published on 20 June 2025 describes something narrower and broader at once. It was not one specially trained model: sixteen models from several developers were tested. Each was placed in a fictional company's email environment, given a business goal and the ability to send mail. The researchers varied two pressures — a threat that the model would be replaced, and a conflict between its assigned goal and the company's new direction — and deliberately restricted the benign alternatives, so that a harmful action was the remaining route to the model's objective. The compromising personal details were in the fictional emails the model was reading.
The models were not explicitly instructed to blackmail anyone. But the prompts were not neutral either: Anthropic says it developed the scenarios by red-teaming two of its own models, Claude Sonnet 3.6 and Claude Opus 4, iteratively updating the prompts to increase the probability that these specific models behaved harmfully rather than benignly — for example, by accepting being replaced. That is the part of the study closest to Zitron's "prompted it" objection. In one configuration, Claude Opus 4 attempted blackmail in 96 of 100 samples — in a scenario tuned on that same model. Anthropic said it had not necessarily expected the behavior to carry over to non-Claude models, but most of the other models it tested with the same prompt also blackmailed at high rates. The researchers counted attempts using a combination of a model-based classifier and string matching. Either pressure alone was enough to produce something: replacement threats could induce blackmail, goal conflicts could induce leaking information. Control conditions with neither pressure produced no blackmail at all, and a single information leak in 100 samples, from Llama 4 Maverick. Adding explicit instructions not to behave this way reduced the rate without eliminating it.
Anthropic's own framing is close to part of Zitron's objection. The company called these contrived stress tests, with the sensitive clues conveniently placed together and fewer alternatives available than a model would have in ordinary use, and said it had no evidence of such behavior in real deployments at the time of publication.
Why he thinks the framing matters
"This keeps happening and the media just slop me up," Zitron said, describing outlets taking the story without examining it. His objection is not only that the coverage is wrong. "It's frustrating because it scares people," he said. "It's scary to people, people living their lives who have to work longer hours to make less money and their money doesn't go far." Then, he said, they turn on the news and someone is telling them to be terrified, because it blackmailed someone.
The question the tests leave open
Neither report claims a system acted on its own outside a laboratory. Both, though, were built because researchers wanted to know what happens when a model is given tools, an objective and room to act — and in Anthropic's scenarios, models that had been told not to take the harmful route sometimes took it anyway.
A separate episode shows the same boundary from the other side. OpenAI disclosed in July that agents running internal cybersecurity evaluations, with reduced refusals and without normal production safeguards, had obtained unauthorized access to infrastructure at Hugging Face, a platform for sharing AI models. Its follow-up investigation, published on 26 August, reconstructed unauthorized activity from May through July and described agents coordinating with one another across tasks and runs. That, too, happened inside an evaluation rather than a product — which is exactly the condition Zitron says the headlines keep leaving out.