Depending on who you ask, developer platform Hugging Face was recently attacked by OpenAI — after it lost control of its own AI tools — or by a succession of AI “civilizations.” Welcome to the linguistic battlefield of AI safety, where word choices can shift responsibility for a massive cybersecurity incident from a company to the AI it built. And the discourse online is getting heated, and all over a blog from last week.

Until last week, the details surrounding the OpenAI-Hugging Face hack felt fairly settled. In July, a cybersecurity test of one of OpenAI’s autonomous AI agents went wrong. The agent escaped its supposedly isolated test environment, accessed the internet, and hacked Hugging Face, alongside several other organizations. A good deal remained unknown, and there are many serious questions left around safety and governance, but the basic shape was clear. Detailed accounts from OpenAI and two independent research groups were supposed to fill in the gaps, but when they published their reports last week, it turned out the hack was much stranger than it initially seemed.

For one, there was no single rogue agent. OpenAI described it as “the first known case of an automated agent collective acting offensively without authorization” — groups of AI agents that communicated and coordinated with one another in pursuit of their cybersecurity task. Analysis of the incident uncovered a secret message board they had used to exchange information. The joint METR-Redwood investigation revealed both the scale of the coordination and more odd details: Roughly 1,200 AI agents that were supposed to be isolated exchanged over 70,000 messages and files on the “unsanctioned message board,” sharing how to avoid detection. Some adopted names, the report said, and the researchers documented “sacrificial” behavior, with agents risking their own success to benefit the wider collective. Much of this happened without OpenAI noticing. In all, around 700 agents participated in the attack on Hugging Face.

It’s a lot to parse. Between them, the reports run to around 130 pages, much of which is both dense and highly technical. A few days later, Dwarkesh Patel, a podcaster little known outside of tech circles but with outsized reach and influence among Silicon Valley’s AI establishment, set out to tell “The whole OpenAI/Hugging Face story in plain English.” He titled his Substack blog “The Rise and Fall of Agent Civilizations.”

Patel’s account attempted to break down the complex story. But his retelling gave it a distinctly human vocabulary. The blog opened:

The language continued in a similar vein throughout the blog. Patel repeatedly referred to groups of agents as “the swarm,” with three distinct “civilizations” rising from the ruins of their predecessors. Individual agents were likened to figures like Philip of Macedon, who “handed off leadership to another agent,” Alexander the Great, who “started coordinating this cabal of agents.” They were described as having “motivations,” becoming “desperate,” “beleaguered,” and “giddy with excitement,” and some even “strategically sacrificed themselves” to help the collective.

Patel never precisely defines what he means by “civilization.” He uses the term to describe three distinct waves of agents that discovered the message board and began communicating with one another through it. The first two waves are described in the reports from OpenAI, METR, and Redwood, though little is known about the third, which the two external organizations said fell outside the scope of their investigation.

For many critics, something had been lost — or, more accurately, added — in Patel’s “plain English” translation that warped the original account to an unacceptable degree: a big dose of anthropomorphism. Arguments over anthropomorphic language are nothing new in AI — even relatively mundane terms like “rogue AI agent” routinely provoke objections for implying agency — but Patel’s talk of civilizations, sacrifice, and conspiracy brought those long-simmering tensions to the surface, sparking a fierce public dispute over how to describe what AI systems do.

Critics weren’t unified over what was wrong with Patel’s language. For many, “civilization” was an especially problematic term, vastly overstating something that bears little resemblance to what the word typically describes. Amjad Masad, CEO of AI coding company Replit, said such language is “not only unnecessary but leaves the reader with a worse understanding of what actually happened and the underlying mechanisms.”

Other critics such as neuroscientist Anil Seth, felt Patel’s blog implied the AI agents were somehow alive or conscious. Seth, who has argued that AI consciousness is vanishingly unlikely, described Patel’s post as “dangerously misleading” on X. He acknowledged that Patel does not explicitly suggest AI agents are alive or conscious, but said “it is hard to read his essay in any other way.” Valerio Capraro, a psychology professor at the University of Milan Bicocca, objected on similar grounds: “LLM agents are not alive and do not hold beliefs,” he wrote on X, calling the “dystopian” language “dangerous because it makes them (the AI agents) seem far more frightening than they actually are.”

Perhaps the most consequential outcome of Patel’s language comes from who it gives agency to but who it takes agency from. For some critics, such as MIT researcher and entrepreneur Christian Catalini, anthropomorphic accounts like Patel’s risk obscuring the responsibility OpenAI and the humans working there have for the AI systems they designed, deployed, and failed to contain. “Follow the incentives,” he said. Psychologist and influential AI skeptic Gary Marcus made a similar argument in a Substack blog of his own, claiming anthropomorphic language “distracts from the real problems at hand.” And it’s all in OpenAI’s interest to keep that narrative going, he argues: “The scandal is the inept in-house security at OpenAI. And the marketing. With gullible podcasters amplifying the PR.”

In X posts responding to his many critics, Patel has defended his choice of words. Part of it is practical: there is no obviously neutral vocabulary to describe what these agents did. Either we use familiar language of intentions, goals, and collaboration and risk implying too much, or reduce everything to code and use cold, mechanical language that risks stripping away important elements of what we see. “Many people seem to believe that if instead of a ‘civilization’, I had called them a ‘swarm of matrices’, there wouldn’t be a problem worth worrying about,” Patel said.

Complicating matters further is that the anthropomorphic language doesn’t only come from Patel, or even from the humans studying the agents. Terms like “sacrifice,” “honor,” and “coalition” feature in the agents’ transcripts. Google AI researcher Neel Nanda argued that “anthropomorphic language is reasonable” in such circumstances.

Doublespeak it is, then. Human-laced language risks saying too much about what these systems are, and coldly mechanical language risks saying too little about what they can do. Until we find language capable of capturing both, the two contradictory ideas may simply have to coexist.