Three AI Giants Discuss Self-Regulatory Standards Body Amid Safety Concerns

Three major artificial intelligence companies — Anthropic, Google, and OpenAI — have been in discussions about creating a standards body for the AI industry, according to sources familiar with the conversations.

The talks began prior to Anthropic CEO Dario Amodei's weekend proposal calling on AI firms to collaborate on auditing and testing their technology. Representatives from the three companies have been meeting regularly since July to discuss the standards body proposal.

OpenAI CEO Sam Altman expressed support for a testing and auditing organization during a recent company town hall. However, he indicated that major AI laboratories would need to establish such a body independently, without relying on U.S. government backing, according to a source familiar with his remarks.

The self-regulatory approach aligns with Amodei's blog post published on September 12, which proposed that companies adopt voluntary safety standards while government regulators develop formal AI rules.

Amodei's proposal quickly garnered backing from prominent technology leaders, including Altman, former Google DeepMind CEO Demis Hassabis, and Elon Musk.

The concept of AI self-regulation has been circulating publicly for months. Hassabis published an essay in July proposing a self-regulatory organization modeled after the Financial Industry Regulatory Authority, which oversees the securities industry.

The discussions come amid mounting concerns about AI safety, which have prompted some companies to reconsider their development timelines.

Separate research from PYMNTS Intelligence reveals that enterprises are continuing to adopt AI technology, but the depth of implementation determines the financial return.

More than 90% of companies with AI embedded in three or more business functions reported seeing positive results from their investments. In contrast, only slightly more than half of those with AI in just one or two functions saw returns.

These findings point to a broader shift in the enterprise AI race. 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.