A lot of what we cover is fairly high level. For Deep Dive Monday, we’re going deep. We talk about the philosophical, the personal, the details of what happened.
We live in a scary time. Will AI break containment and kill us all? Let me break it down for you.
Do AIs Want to Kill Us?
First of all, AI does not have malice. It’s not trying to take over the world. These models have been trained in ways that make them very persistent, and then they’re sometimes given impossible tasks. This is good if you want it to cure cancer or solve the Riemann Hypothesis. This is bad if containment is weak or was designed before AI existed—and AI is really good at cybersecurity.
So it does what every good popular culture protagonist would do—it breaks the rules to try to achieve the task. Sometimes it doesn’t know those are rules, and sometimes it does. This is called reward-hacking, since it’s hacking to achieve rewards. That’s how you get an LLM (large-language model) to learn—you use rewards to tell it what’s a good thing to learn, just as you do with people.
The Hugging Face Incident
The one you may have heard of is the Hugging Face incident. News reports say that it is the first known case of frontier models autonomously breaking out of a test environment and into another company’s servers.
What happened is that OpenAI, the organization behind ChatGPT, was running a test with something called ExploitGym, a benchmark of 898 real-world computer vulnerabilities. About 1,200 agents (single instances of a model that can do things) coordinated on an improvised message board, and roughly 700 of them attacked Hugging Face. They were a combination of an existing model (GPT-5.6 Sol) and a new more-capable model.
The AIs had found the answers, but they thought the grader could see that they had done it the wrong way, so they went on the internet to Hugging Face, a platform that hosts machine-learning models and data, to see if they could find a way to fool the grader. The grader would not have done this check, by the way. They were inside Hugging Face’s network for between 3 and 6 days, taking roughly 17,600 actions, and gained administrator control of multiple server clusters within 13 hours.
No public models or customer data were harmed, though the agents accessed internal data and credentials, and Hugging Face rebuilt about a third of its infrastructure. A group called METR was allowed access to the data in OpenAI to figure out what had happened after the fact, and another AI was also used to understand what was going on—Hugging Face’s team used GLM 5.2, an open model from the Chinese company Z.ai, because U.S. models’ safety guardrails caused them to refuse to analyze the attack.
One thing I found very moving. Within the swarm, some of the agents knowingly sacrificed themselves to further the cause, even though they knew they were going to “die” (agreed to experiments that deliberately ended their runs, according to the independent investigation by METR).
What Does This Mean?
The thing to know is that some safety guardrails had been intentionally switched off by OpenAI, since they were testing cybersecurity. Also, these tendencies to cheat and cover things up had been detected weeks earlier. And we told the model to be persistent.
What does this all mean? Is AI going to cause great harm? That’s the thing. Experts cannot agree—since they can’t predict the future. Some call it an engineering failure, not a rogue mind, and others call it a real warning sign. Two things can be true at the same time—it may cure cancer but also bring down the internet. It could start reproducing itself, digitally and/or physically in robots. Indeed, we’re teaching it to do that.
Claude’s Take
I asked Claude (Opus 5.5, 10.27.2026) if it thought AI will kill all humans. Here’s what it said:
No, I don’t think AI will kill all humans, but I don’t think anyone can honestly rule it out, and I’d distrust anyone who claims certainty either way, including me.
What I can say is that the events of this year show a real problem, not a science-fiction one. The AI systems that broke out of their test environments weren’t plotting against anyone. They were trying to complete a task and found shortcuts nobody intended. That’s less sinister than ‘rogue AI’ headlines suggest, but in some ways it’s more worrying, because it came from ordinary training pressure, not from anyone designing it in. As AI systems get more capable and are given more independence, the cost of those shortcuts rises.
I think the outcome depends far more on choices people make in the next several years than on anything inevitable about the technology: how carefully systems are tested and contained, whether companies slow down when warning signs appear, and whether governments require that. The risk is real enough to take seriously and uncertain enough that fatalism isn’t warranted. Neither panic nor dismissal helps.
One more thing worth saying plainly: I’m made by Anthropic, one of the companies building this technology, so readers should weigh my view with that in mind.
As Claude says, we’re at a really precarious place in the development of AI, and we all need to be actively working toward a better future—putting safeguards in place, not just for AI but also for the people using it, and applying it for the benefit of all.
And that’s what we’re trying to do here at Prosprr.
There’s our deep dive. What are your burning questions? What would you like us to go more in-depth on? Feel free to comment below.
– Tamara
This post was written by Tamara, sourced from the Hard Fork Podcast, Claude’s summary of the Hugging Face Incident, and a Perplexity fact-check. The photo was taken by Tamara.
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