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Eric Schmidt argues against a ‘Manhattan Project for AGI’

By Advanced AI EditorJune 8, 2025No Comments4 Mins Read
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In a policy paper published Wednesday, former Google CEO Eric Schmidt, Scale AI CEO Alexandr Wang, and Center for AI Safety Director Dan Hendrycks said that the U.S. should not pursue a Manhattan Project-style push to develop AI systems with “superhuman” intelligence, also known as AGI.

The paper, titled “Superintelligence Strategy,” asserts that an aggressive bid by the U.S. to exclusively control superintelligent AI systems could prompt fierce retaliation from China, potentially in the form of a cyberattack, which could destabilize international relations.

“[A] Manhattan Project [for AGI] assumes that rivals will acquiesce to an enduring imbalance or omnicide rather than move to prevent it,” the co-authors write. “What begins as a push for a superweapon and global control risks prompting hostile countermeasures and escalating tensions, thereby undermining the very stability the strategy purports to secure.”

Co-authored by three highly influential figures in America’s AI industry, the paper comes just a few months after a U.S. congressional commission proposed a “Manhattan Project-style” effort to fund AGI development, modeled after America’s atomic bomb program in the 1940s. U.S. Secretary of Energy Chris Wright recently said the U.S. is at “the start of a new Manhattan Project” on AI while standing in front of a supercomputer site alongside OpenAI co-founder Greg Brockman.

The Superintelligence Strategy paper challenges the idea, championed by several American policy and industry leaders in recent months, that a government-backed program pursuing AGI is the best way to compete with China.

In the opinion of Schmidt, Wang, and Hendrycks, the U.S. is in something of an AGI standoff not dissimilar to mutually assured destruction. In the same way that global powers do not seek monopolies over nuclear weapons — which could trigger a preemptive strike from an adversary — Schmidt and his co-authors argue that the U.S. should be cautious about racing toward dominating extremely powerful AI systems.

While likening AI systems to nuclear weapons may sound extreme, world leaders already consider AI to be a top military advantage. Already, the Pentagon says that AI is helping speed up the military’s kill chain.

Schmidt et al. introduce a concept they call Mutual Assured AI Malfunction (MAIM), in which governments could proactively disable threatening AI projects rather than waiting for adversaries to weaponize AGI.

Schmidt, Wang, and Hendrycks propose that the U.S. shift its focus from “winning the race to superintelligence” to developing methods that deter other countries from creating superintelligent AI. The co-authors argue the government should “expand [its] arsenal of cyberattacks to disable threatening AI projects” controlled by other nations as well as limit adversaries’ access to advanced AI chips and open source models.

The co-authors identify a dichotomy that has played out in the AI policy world. There are the “doomers,” who believe that catastrophic outcomes from AI development are a foregone conclusion and advocate for countries slowing AI progress. On the other side, there are the “ostriches,” who believe nations should accelerate AI development and essentially just hope it’ll all work out.

The paper proposes a third way: a measured approach to developing AGI that prioritizes defensive strategies.

That strategy is particularly notable coming from Schmidt, who has previously been vocal about the need for the U.S. to compete aggressively with China in developing advanced AI systems. Just a few months ago, Schmidt released an op-ed saying DeepSeek marked a turning point in America’s AI race with China.

The Trump administration seems dead set on pushing ahead in America’s AI development. However, as the co-authors note, America’s decisions around AGI don’t exist in a vacuum.

As the world watches America push the limit of AI, Schmidt and his co-authors suggest it may be wiser to take a defensive approach.



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