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The US and China are competing for global leadership in artificial intelligence as the technology rapidly advances. Photo: Shutterstock

The United States should start preparing for scenarios where extreme measures must be taken to stop China from achieving artificial general intelligence (AGI), according to a former White House official, including state-backed espionage and military strikes on Chinese data centres.

Jacob Stokes, deputy director of the Indo-Pacific Security Program at the Centre for a New American Security (CNAS), said at an online event on Thursday that various US agencies, including the Department of Defense and the National Security Agency, should begin assessing what intelligence they need to justify taking such actions.

“Trying to think through the particulars of that will be especially important, in part because it will help policymakers … start to work backwards based on the unique nature of the technology, in the same way that in a past era, policymakers would learn about nuclear weapons and … work backwards from the science to the policy implications,” he said.

In a new CNAS report published last week, the former Obama administration national security staffer called for the US government to consider the feasibility of diplomatic, espionage, cyber and kinetic measures to prevent China from achieving AGI first.

AGI is a hypothetical AI system that can achieve human levels of performance across a wide variety of tasks. The report, titled “Superpowers and AGI”, explores how the imminent arrival of AGI might affect US-China geopolitical competition.

The US currently enjoys a months-long lead over China in frontier AI models, according to Epoch AI. However, China could make a “surprise technological breakthrough” that means it is close to achieving or achieves AGI first, Stokes wrote, citing China’s advances in areas such as robotics and other forms of “embodied AI”.

If this were the case, the US government should consider extreme actions such as military strikes on the data centres that train and run the Chinese AGI system, Stokes wrote, including preventive strikes before China develops AGI.

As AI technology grows in importance economically and militarily, the data centres that form the backbone of the AI industry have emerged as military targets. In March, Iran bombed Amazon data centres in the United Arab Emirates and Bahrain as part of its conflict with the US.

According to Stokes, US government agencies would have to work out what facilities to attack and with which weapons, while considering the risk that the strikes lead to a general war between the two countries.

William Hartung, an arms analyst at the Project on Government Oversight, a Washington non-profit organisation, also warned that any strikes on Chinese data centres would “risk starting a shooting war between two nuclear armed powers that could have tragic consequences for all concerned”.

“Striking Chinese data centres based on how they might or might not be used in the future would be the height of recklessness,” he said.

Another option would be to reverse engineer or “steal” China’s AGI technology, Stokes wrote, which would be justified “legally and normatively” because of China’s own alleged history of stealing US intellectual property.

However, he noted that the US government and intelligence apparatus have “not been organised for espionage to gather technological secrets for decades”.

The stark assessment comes as US President Donald Trump and Chinese President Xi Jinping are set to have talks in Washington later this month on AI governance as the technology continues to improve, posing growing risks to national and global security.

The “best case scenario” for the talks is that the two sides find the talks “constructive”, Stokes said on Thursday, rather than any expectations of any breakthroughs on key governance issues.

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[–] 1 point 10 hours ago* (1 child)

Well what is then? Because the most common definition of AGI among researchers is that it’s an AI model capable of adapting to any economically productive task. AGI adoption would require that model to be more efficient than human workers either by doing things faster or by doing things cheaper than human employees, this is why AGI adoption would come much later than AGI.

And GPT 6 Astra is quite close theoretically, I mean someone prompted it to play a virtual piano and it did so in real time after it learned how to use the program through trial and error. Keep in mind this model will be deeply out of date in a year.

But it really doesn’t matter what you call it, if it’s AGI or just a stochastic parrot, nor does it matter if it’s conscious, sapient, sentient, whatever, if it’s capable of doing the necessary tasks in order to effectively replace the labor a human does and in a cost effective and timely manner that will lead to mass job loss.

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  • [–] 2 points 8 hours ago (1 child)

    Because the most common definition of AGI among researchers is that it’s an AI model capable of adapting to any economically productive task.

    I would want sources on such a claim (though it's also a claim that is hard to prove, given you're saying "most common among researchers" and it's unlikely that most researchers are focused on / talking about AGI when they have much more realistic and smaller goals to put their time into, as incremental development of the technology in one form or another). In the time I've been around gen AI as tech (granted, not as a researcher myself) I've never come across researchers who speak of it in such a way. It sounds more like the kind of thing that investor hype people like Sam Altman would peddle.

    The way I've understood AGI in the past, in a more grounded meaning of it, is that it's a way of talking about a theoretical AI that has generalized capabilities, rather than being strictly confined to one domain. However, this need not inherently mean we're talking about something that is both generalist and specialist at the same time, in every task imaginable. That's logistical absurdity.

    Everything has tradeoffs and AI bubble salespeople have a bridge to sell. Behind them is the cold hard logistical realities of AI research. Some of which is real progress (I've seen some of it in practice), but the real progress is leagues behind the picture that the salespeople present. In actual practice, companies are finding it harder to get real value out of AI than they thought it would be. LLMs tend to be the leading marketing image of AI progress, but have major infrastructure limitations.

    China's dark factories are probably far more of a logistical breakthrough than most of the attempts to shoehorn LLMs into a workplace in the west. Automation is real and does real stuff, but AI is not a panacea and is not on track to be one any time soon.

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  • [–] 1 point 7 hours ago

    Well said honestly. I just think underestimating AI is a loosing battle because it’s an exponential development so the picture will be drastically different a year from now and even more drastically different another year from now. I mean just a few years ago models couldn’t even form an essay properly and now they’ve already become the standard in coding and are posing actual cybersecurity risks

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