That’s according to a report Sunday (Sept. 13) from The Information, citing sources familiar with the matter. These sources said the discussions began even before Anthropic CEO Dario Amodei’s weekend call for artificial intelligence (AI) companies to work together on auditing and testing the technology.
Meanwhile, OpenAI CEO Sam Altman said at a recent company-wide town hall that he was in favor of a testing and auditing organization for the industry, but believed that major AI labs would have to establish a standards body on their own without the backing of the U.S. government, a source familiar with the comments told The Information.
As the report notes, this sort of industry self-regulation was included in a proposal Amodei published Saturday (Sept. 12) on his blog, calling for a slowdown in AI development amid concerns about the technology’s safety.
He argued that companies could move forward with voluntary safety standards while the government works on AI regulation.
According to the report, Amodei’s post soon gained the support of numerous other tech leaders, including Altman, former Google DeepMind CEO Demis Hassabis, and Elon Musk.
The notion of a self-regulatory group for AI has been part of the public conversation for months, The Information added. Hassabis published an essay in July proposing a self-regulatory body for AI modeled after the Financial Industry Regulatory Authority.
However, the report said, the extent to which companies have begun collaborating on industry standards has not been reported until now. Sources told The information that representatives from Google, Anthropic and OpenAI have been regularly meeting since July about the proposal for a standards body.
In other AI news, recent PYMNTS Intelligence research finds continued enterprise adoption of the technology, although it is the companies with a deeper adoption of the technology who are more likely to see a payoff.
According to “AI at Work: Why Deeper Enterprise Use Produces Stronger Returns,” more than 90% of companies with AI embedded in three or more functions say they are seeing their efforts pay off. By contrast, a little more than half of those with only one or two embedded AI functions reported seeing a return.
“These findings point to a broader shift in the enterprise AI race,” the report said. “Depth of AI tool usage, not breadth, is driving financial outcomes. That suggests that spending more and using AI in more places aren’t necessarily enough to generate a bang for the buck.”
