Cowen Urges Quantitative Approach Over Panic After Hugging Face AI Security Incident
Key Takeaways
- •The recent Hugging Face hack drew wide attention because the platform is significant infrastructure for hosting AI models and datasets.
- •Several commentators, including AI safety figures Nate Soares and Ajeya Cotra, described the incident as a warning sign of an imminent AI takeover.
- •Cowen argues that a coordinated national and international effort, though expensive, is needed to limit future episodes of powerful AI models going rogue.
- •Cowen compares the reactions to past overreactions to real problems such as DDT, Y2K, global warming, Covid, thalidomide, and nuclear power.
- •Cowen urges discussing AI cybersecurity risks with quantitative estimates, including expressing expected costs as a percentage of GDP.

The recent Hugging Face hack is the subject of economist Tyler Cowen's latest Free Press column. Hugging Face, one of the largest open platforms for hosting AI models and datasets—used widely by researchers and companies building on open-source machine learning—has become a significant piece of infrastructure in the AI ecosystem, which is part of why a security incident there has drawn such attention. Some observers have reacted to the incident in dramatic fashion.
One commentator is worried about a "full-blown AI takeover within months," and another wrote that he was "feeling a bit sad about our impending extinction." Nate Soares, who works in the AI safety movement and is co-author of the doomsday AI bestseller If Anyone Builds It, Everyone Dies, wrote that "This might be the last warning we get." Cotra said that the incident "feels like it's more than 50 percent of the way to full-blown AI takeover."
Cowen, an economist at George Mason University known for his writing on the economics of technology and for his Marginal Revolution blog, suggests a different, more technocratic approach:
As he wrote in The Free Press last week, he argues that a coordinated national and, indeed, international effort is needed to limit such episodes of a powerful AI model going rogue in the future. That is likely to be expensive, and there is no easy, complete solution at hand for any amount of money.
Nonetheless, Cowen sees people committing the same mistake that Americans have made many times before. "They are moving into the mode of the hysterical, the anecdotal, and they are letting emotional reactions bypass reason and quantitative estimates."
He cites a few other examples: DDT in the 1960s and 1970s, the Y2K "crisis" of 2000, global warming, Covid, thalidomide babies, and nuclear power. All of those represented—or still represent—very real problems. Yet each time, he argues, we drastically overreacted, letting ourselves get swept up in a climate of fear after one emotionally vivid incident, often reported breathlessly.
When it comes to the cybersecurity risks from advanced AI, Cowen writes that they very likely will not come close to being as bad as either Covid or global warming. But the same logic of exaggeration is operating, with amplification through social media and through the negativity and pro-pessimism biases of mainstream media.
And so he has a plea: "If you are going to talk about the problem, please offer a quantitative estimate of what you think the cybersecurity costs from advanced AI will be over the next year or two. Better yet, express that number as a percentage of gross domestic product. Simply put, how much would it realistically cost to create the basic safeguards that we all agree are essential? And what would be the remaining damages from problems we cannot control?"
Those who are worried about the risks of AI systems, Cowen writes, seem intent on proving the seriousness of their concerns. "But they are falling into all-too-common emotional overreactions of our past, rather than focusing on the quantitative and scientific." He concludes by recalling the saying of Scotty, the chief engineer in the classic Star Trek: "Fool me once, shame on you. Fool me twice, shame on me."
Cowen encourages readers to read the whole column.