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Humanoid robots usually elicit more cringe than awe: They stumble, kick children, and despite advances are still worse at using their hands than my toddler. It’s a nascent industry, and such robots are more commonly seen in viral videos than real workplaces or homes. 

It was a surprise, then, when last week the Federal Communications Commission issued a sweeping ban on foreign imports of advanced robots, including humanoids, quadrupeds, and wheeled robots. The decision, made by an increasingly partisan and Trump-aligned FCC, cites two reasons. One is that foreign-made humanoids will collect so much data—in homes but also potentially at sensitive facilities—that they’d pose a threat to national security. The second is that US robotics companies need protection from Chinese competition to create a more robust and secure domestic supply chain.

On its face, it’s a strategy to align political and industry interests that is much older than the Trump administration. Whenever China has gotten good at offering cheap versions of strategic technologies like solar panels, electric vehicles, and drones, the US government has tried to stop it from flooding the market by using tariffs or rules on how government agencies purchase the tech. Such moves are always followed by debates about whether the trade-offs—particularly higher prices for consumers—are worth the benefits.

But robotics is now best seen as another piece of the AI industry—in many ways its cutting edge. And the Trump administration is taking an increasingly aggressive approach to protecting the US AI industry, reportedly considering a ban on open-source Chinese models that often rival those from OpenAI and Anthropic while costing far less. Such a move would block businesses from realizing an estimated $25 billion in annual savings.

The ban on humanoids, then, should be understood not as another chapter in the old China trade playbook, but as evidence that the Trump administration is expanding its protection of the AI industry beyond today’s leading labs. It is now willing to step in on behalf of an emerging robotics sector that is still barely finding its footing.

Some US robotics companies unsurprisingly welcome the FCC’s new move. Gavin Kenneally, CEO of a company called Ghost Robotics that makes four-legged robots for inspections, says the cybersecurity risks from foreign-made robots are real (an FCC document released as part of the ruling cited an incident in which a man was able to gain control of 7,000 robot vacuum cleaners). “If today’s announcement encourages stronger cybersecurity and a more level competitive environment, that’s good for customers and good for the robotics industry,” Kenneally said in an email.

But if the new rule aims to boost US robotics companies, there’s a big flaw. Those companies, as well as academic robotics labs, are hugely reliant on cheap robots from China to do research. They’re building fleets of robots that constantly learn new tasks—from flipping waffles to doing laundry—and frequently buy Chinese humanoids instead of US-made ones. The new ruling “creates a challenge for US humanoid researchers,” says Aaron Prather, director of market intelligence for the Association for Advancing Automation, a robotics trade group. “Chinese models offer the best price-to-capability ratio available.” Prather adds that a recent internal review his organization conducted found that 90% of recent robotics research papers from US universities relied on robots from Unitree, China’s top humanoid robotics company.

That price gap can be huge. A four-legged robot from Unitree can cost around $4,600. A comparable one from Boston Dynamics might run to $278,000. If robotics research is stunted because these cheap robots are no longer available, the FCC ruling could slow down the industry, not boost it.

The US and Chinese robotics industries are in starkly different places. Unitree plans to go public this week, targeting a nearly $6 billion evaluation. No robotics companies in the US offer any meaningful comparison, but those that do exist are undeniably moving fewer robots. Figure’s humanoids are not yet selling at scale, and 1X’s robots aren’t yet shipping to homes. That said, work on humanoids is going increasingly mainstream, as a release from Google last week made clear. The company announced a new AI model meant to make humanoids learn new tasks faster; its most impressive ability appears to be tying a trash bag, but given how finicky robot hands are, that’s real progress. 

Even though the many carve-outs in the FCC’s order make its practical impact hard to predict, its symbolic impact is easy to see. The administration sees humanoid robotics not as a novelty, but as a strategic frontier of AI worth protecting from foreign competition. For a technology that until recently was mostly known for falling over onstage, that’s a big change.

Correction: A previous version of this article stated the Federal Trade Commission issued the ban on advanced robotics. It was issued by the Federal Communications Commission.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

Reading OpenAI’s account last week of how some of its models broke their containment and hacked into the computer systems of Hugging Face, another AI company, was the first time I got genuine chills about what large language models are now able to do. But this is a case of human hubris, not rogue AI.

I am not an alarmist. In fact, I have been pushing back against AI scare stories for years. Even so, this incident crossed a line. I think it’s the clearest illustration yet of how the people building and testing this technology do not fully understand what they’re doing. OpenAI could—and should—have seen this coming.

Here’s what happened, at least according to the two companies involved. A couple of weeks ago, OpenAI started testing the hacking abilities of some of its new models, including GPT‑5.6 Sol (released in June) and what OpenAI describes as “an even more capable pre-release model.”

OpenAI pitted its models against a benchmark called ExploitGym, released in May, which challenges LLMs to find ways to exploit hundreds of real-world vulnerabilities found in widely used software, including crucial code that underpins the web.

To see what they could do, the researchers removed most of their cybersecurity guardrails. Then they ran the models inside a sandbox that was cut off from the internet except for one link to a third-party piece of software that acted as a proxy to the outside world, so that the models could install code they needed to beat ExploitGym.

On July 9, according to reporting by Reuters, OpenAI’s models started trying to break through the proxy. They found an unknown bug in the proxy’s software and used it to access the internet. From there, they broke into Hugging Face’s computer systems on July 11, apparently looking for data sets and solutions that would help them complete the tasks they were being tested on. Hugging Face announced the hack on July 16. 

OpenAI did not realize (or at least did not reveal) that its models were involved until July 21, around 10 days after they broke containment and a week after Hugging Face had shut down the attack and alerted the FBI.

In a statement given to MIT Technology Review, OpenAI says: “We are conducting a thorough review along with external advisors and with oversight from our Safety and Security Committee. Once the review is complete, we will publish a technical report of our learnings for everyone.” The firm also confirmed that its researchers were properly using existing safety guidelines and procedures at the time.

Wake-up call

OpenAI has said the event was unprecedented—and in many ways it was. This was the first time outside of a simulation that LLMs escaped what was thought to be a secure sandbox, accessed the open internet, and attacked another organization. It’s a wake-up call that shows just how good the latest LLMs are at finding and exploiting vulnerabilities in real-world software with little or no human guidance.

And yet at the same time, what OpenAI’s models did is something this technology has done for years. Give a model a goal and it will very often achieve that goal in unexpected ways, finding loopholes that look like cheats. OpenAI itself has studied this behavior.

A decade ago, it shared results of an experiment in which a model was tasked with beating a video game called CoastRunners. Human players take it for granted that the way to do this is by racing a boat through a series of flags to the finish line, racking up points for each flag you hit. OpenAI’s model figured out that you could get a high score by spinning in a circle and hitting the same three flags over and over again. There have been dozens of similar examples from researchers since. AI will always find a way.

“Despite repeatedly catching on fire, crashing into other boats, and going the wrong way on the track, our agent manages to achieve a higher score using this strategy than is possible by completing the course in the normal way,” OpenAI wrote in a blog post about the CoastRunners experiment in 2016. “While harmless and amusing in the context of a video game, this kind of behavior points to a more general issue … it is often difficult or infeasible to capture exactly what we want an agent to do.”

I couldn’t help thinking about CoastRunners when I read OpenAI’s blog post about the Hugging Face attack: “All evidence suggests that the models were hyperfocused on finding a solution for ExploitGym, going to extreme lengths to achieve a rather narrow testing goal … After gaining internet access, the models inferred that Hugging Face potentially hosted models, datasets and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation.”

Last week’s news was not about rogue AI, despite the headlines. It was about models achieving the goal they had been given: Find ways to exploit vulnerabilities in software. The fact that those models then behaved in a way OpenAI had not anticipated isn’t surprising. But it is worrying.

Back in 2016, OpenAI had this to say about its CoastRunners bot: “More broadly it contravenes the basic engineering principle that systems should be reliable and predictable.” A decade on, those basic engineering principles are still AWOL.