Former Google chief executive Eric Schmidt says China’s approach to artificial intelligence differs sharply from that of the United States, with potential consequences that extend beyond technology into global politics.
In a panel discussion at the All-In Summit, Schmidt says that unlike America, China is handicapped in terms of raising billions of dollars in capital within a relatively short period.
But the former Google executive says the country has adapted by doubling down on applied uses of AI in ways that could challenge American dominance in key industries.
“They’re not pursuing crazy AGI strategies, partly because of the hardware limitations that you’ve put in place, but partly because the depth of their capital markets don’t exist.
They can’t raise, based on a wing and a prayer, $100 million, or maybe a renminbi equivalent, to build the data centers. They just can’t do it. And so the result is they’re very focused on taking AI and applying it to everything.
And so the concern I have is that while we’re pursuing AGI, which is incredibly interesting and we should talk about, and all of us will be affected by this, we better also be competing with the Chinese in day-to-day stuff: consumer apps, robots, and so forth and so on.
I saw all the Shanghai robotics companies, and these guys are attempting to do in robots what they’ve successfully done with electric vehicles. And their work ethic is incredible. They’re well-funded. It’s not the crazy valuations that we have in America. They can’t raise the capital, but they can win across that.”
Schmidt also highlights a divergence in how the two nations approach openness in AI models. He warns that China’s commitment to open weights and training data could tilt international adoption away from American technology.
“The other thing the Chinese are doing — and I want to emphasize this is a major geopolitical issue — is that my own background is open source. In the audience, you all know open source means open code. Open weights means open training data.
China is competing with open weights and open training data, and the US is largely and majority focused on closed weights and closed [training] data. That means that the majority of the world, think of it as the Belt and Road Initiative, are going to use Chinese models and not American models.
Now, I happen to think the West and democracies are correct, and I’d much rather have the proliferation of large language models and that learning be done based on Western values.”
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