Big AI Slowdown A Safety Pause Or Strategic Retreat?

The AI situation has an extra twist: the industry is in the middle of a gold rush. Scientific rivals may be able to restrain themselves, but commercial rivals rarely hold back.

There are clear precedents for self-regulation failing in the face of competitive pressure. In the Boeing 737 Max disaster in 2018, for example, pressure to catch up to rival Airbus led Boeing to hide the limitations of the Max, which ultimately cost lives.

In the AI race, the scale of the competitive tension is even greater.

Anthropic and OpenAI are competing to establish market dominance before pursuing initial public offerings (IPOs) that could raise tens or even hundreds of billions of dollars.

And at the nation-state level, the stakes are higher again, with the US and China each hoping to use the new technology for geopolitical advantage.

Amodei's slowdown proposal centers around a three-step plan: embedding independent third-party safety reviewers, establishing coordinated industry safety standards within democratic nations, and eventually securing global agreements.

This would include strict limits on AI chip exports to companies and countries that do not agree to prioritize AI safety.

Anthropic and OpenAI have already agreed to the first phase of this plan, despite their history of suing, publicly insulting, and undercutting each other in pursuit of market dominance.

While recent high-profile hacks may have forced their hands, the leading AI companies may benefit from a development pause or slowdown. For one thing, it could put off strict legislation such as US Senator Bernie Sanders' proposed Ban Artificial Superintelligence Act.

For another, by creating expensive safety standards and limiting chip exports, it could block smaller competitors – especially Chinese companies such as DeepSeek and Alibaba – from catching up. A pause would also give the overstretched frontier labs a chance to recoup, recover, and focus on profits over progress.

These AI labs have spent enormous amounts to produce their existing models, which must be repaid. At the same time, progress is hitting speed bumps as new high-quality training data gets harder to find, and building the massive data center infrastructure needed to sustain development is a challenge in itself.

A coordinated safety pause could be a convenient public reason for a plateau in AI model performance.

Making AI safe won't be easy. The governance of software is notoriously difficult, as past attempts to legislate encryption software have shown.