Mark Zuckerberg paid $14billion for Alexandr Wang's AI; Meta's own engineers still reach for Claude

Published 15 June 2026 · tech

A year after Mark Zuckerberg spent more than $14 billion to bring Alexandr Wang and a team of Scale AI engineers into Meta, the social

A year after Mark Zuckerberg spent more than $14 billion to bring Alexandr Wang and a team of Scale AI engineers into Meta, the social media giant has its first proprietary frontier AI model. What it does not yet have is proof that Muse Spark can close the gap on Claude, Gemini and ChatGPT, or persuade investors that the company's aggressive AI pivot will amount to anything more than another expensive detour. Why Meta abandoned open source for Muse Spark For years, Meta's identity in artificial intelligence was built on Llama, its family of open-weight models that briefly positioned the company as the industry's most accessible AI developer. That reputation took a significant hit in April of last year, when the release of Llama 4 failed to generate meaningful excitement among developers, prompting Zuckerberg to fundamentally reconsider the company's direction. Also Read | Zuckerberg pumping $145 bn into AI admits Meta made mistake in workforce shift Two months later, Zuckerberg announced a $14.3 billion investment to acquire roughly half of Scale AI and, more critically, to bring Wang on board as Meta's Chief AI Officer, along with his most senior engineers. Wang's first major delivery was Muse Spark, released in April of this year and representing Meta's first step away from open source into proprietary frontier model territory. Wang has since offered a more careful framing of Meta's open-source commitments, saying the company will continue releasing models it judges "fit and safe" to publish, while keeping frontier work locked down. Asked whether Llama remains the brand for that effort, Wang sidestepped the question: "We have exciting debates about branding internally and nothing to share right now." Why Muse Spark stayed proprietary: the biosafety concern The decision to keep Muse Spark proprietary was not purely commercial. Wang acknowledged on Bloomberg Tech that internal testing flagged safety concerns that made an open release untenable. "It actually triggered some high risk areas in the course of early training, particularly around bio risk, but also a number of risks were elevated," Wang told Bloomberg. He added: “This is something I think the entire industry has seen as models improved dramatically over the past year.” Also Read | The teachers getting $50,000 bonuses thanks to a massive Meta data center As part of forming Meta Superintelligence Labs, Wang updated what he describes as the company's advanced AI scaling framework, an internal document outlining how Meta evaluates and mitigates model risks.

Muse Spark's deployment within Meta's own products, he has argued, allows the company to apply safety guardrails that would not exist once model weights are made public. How Muse Spark was built and where it sits in Meta's ecosystem Rather than targeting third-party developers, Muse Spark was designed to integrate directly into Meta's core applications, including Facebook, Instagram and WhatsApp, as well as AI-powered hardware such as the Ray-Ban Meta glasses, according to Thomas Randall, an analyst at the Info-Tech Research Group. It also underpins the standalone Meta AI app and website. "There'll be a lot of these frontier model providers that will fundamentally change in lots of different ways, and Meta needs to have a consistent, reliable proprietary model that they themselves own," Randall said. He added that Meta would be "lost" if Zuckerberg had not opened his wallet for Wang and other high-profile AI hires, describing the move as a "strategic rebuild" for the company. Randall acknowledged that Meta has not taken the "most optimized route," but said he can now see "a vision for what they're trying to achieve and what Wang has been trying to achieve." Why Muse Spark still trails Claude and Gemini For all of that repositioning, Muse Spark has not yet landed as a credible frontier challenger. The Financial Times reported that Meta employees asked to test the model for software development tasks have continued to prefer Anthropic's Claude. Wang has acknowledged that the model trails rivals in coding, even as it has drawn praise for visual understanding. Some insiders, according to the FT, have compared parts of the system to DeepSeek's latest model, while others note that Muse Spark leans on Llama 4 code and datasets, despite Wang previously describing it as built "from scratch." Access has also been narrow. The model lives primarily inside Meta's own applications, with a private API rollout described by the FT as limited. A Meta spokesperson said the company is "already testing with some early partners, and look forward to releasing it this month." The developer trust problem Meta has not solved Beyond safety and performance, Meta faces a more fundamental challenge: rebuilding credibility with the developer community it alienated through the Llama 4 disappointment.