Meta CEO Mark Zuckerberg: Company has caught up with OpenAI and Anthropic in AI race and refuses to slow down development

Meta CEO Mark Zuckerberg stated in an interview with the Financial Times that the company has caught up with OpenAI and Anthropic at the forefront of artificial intelligence and refuses calls to slow down development.

The interview coincided with Meta's release of the Muse Wonder model. Zuckerberg mentioned that this system will allow for more complex behaviors, placing it at the current forefront of AI. Meta claims that Muse Wonder outperforms Anthropic's Claude Opus 5.5 and Fable 5.1, as well as OpenAI's GPT-6 Astra on certain technical benchmarks.

The company plans to integrate the new model into the Muse application, which has become one of the highest downloaded apps in the U.S. Apple and Android app stores. Zuckerberg stated that the company is preparing for the next phase, with model development likely to accelerate significantly from this point, and he promised continued large-scale investment.

He downplayed warnings about the risks of technology going out of control, believing that each lab has its own incentives and capabilities to ensure safe progress, without the need for industry coordination to slow down. Previously, he had delayed the Muse release for months due to safety concerns but did not request other companies to pause their developments.

Since last year, Meta has adjusted its AI leadership, built data centers, and hired researchers at high salaries through large-scale spending to catch up with OpenAI, Anthropic, and Google. The company's market value is approximately $1.8 trillion, and the new model will be used for agent and programming tasks, initially targeting developer tools and subscription users.

Market Mechanism: Zuckerberg publicly claimed that Meta has reached the forefront of AI and refused to slow down the industry, with the event driven by the release of Muse Wonder and benchmark comparisons; capital continues to flow from advertising business to data centers, model training, and talent recruitment, benefiting large platforms that can sustain large-scale investments and integrate products (to maintain competitiveness), while putting pressure on labs that rely on coordinated slowdowns or capital constraints (facing speed and share pressures).