In late March, the Future of Life Institute released an open letter (and a related FAQ) calling "on all AI labs to immediately pause for at least six months the training of AI systems more powerful than GPT-4. This pause should be public and verifiable, and include all key actors. If such a pause cannot be enacted quickly, governments should step in and institute a moratorium." The letter, which also stated that "Powerful AI systems should be developed only once we are confident that their effects will be positive and their risks will be manageable," was initially signed by over a thousand people, including many notable technology leaders. Many thousands more added their signatures after its publication.

A common belief among tech industry insiders is that Silicon Valley has dominated the internet because much of the worldwide network was designed and built by Americans. Now a growing number of those insiders are worried that proposed export restrictions could short-circuit the pre-eminence of US companies in the next big thing to hit their industry: artificial intelligence. In November, the Commerce Department released a list of technologies, including artificial intelligence, that are under consideration for new export rules because of their importance to national security. Technology experts worry that blocking the export of AI to other countries, or tying it up in red tape, will help AI industries flourish in those nations -- China, in particular -- and compete with US companies. "The number of cases where exports can be sufficiently controlled are very, very, very small, and the chance of making an error is quite large," said Jack Clark, head of policy at OpenAI, an artificial intelligence lab in San Francisco.

But "trying to draw a line between what is military and what is commercial is exceedingly difficult," said R. David Edelman, a technology policy researcher at the Massachusetts Institute of Technology. It is difficult to put a "made in America" label on artificial intelligence. Research on the technology is often done collaboratively by scientists and engineers all over the world. Companies rarely hold on to the details of their A.I. work, as if it were a secret recipe. Instead, they share what they learn, in hopes that other researchers can build on it.