Meta's AI Model Delay Raises Market Concerns

Meta Platforms, the social media giant, is currently grappling with a market confidence crisis due to the delayed release of its flagship AI model, Behemoth. The company's engineers have encountered significant technical hurdles in optimizing the model, which was initially slated for a June release but has now been pushed back to the autumn or even later. This delay has raised concerns about the direction and effectiveness of Meta's multi-billion-dollar AI investments.

The Behemoth model, part of the LLaMA 4 series, was highly anticipated as the core of Meta's new AI strategy. It employs a leading-edge mixed expert architecture (MoE), which modularizes tasks to enhance computational efficiency. Derivative versions of this model, Maverick and Scout, have already been made available to developers, showcasing the model's native multi-modal capabilities. These versions can handle multiple media inputs, including text, images, and videos, and have outperformed several top competitors in benchmark tests.

Meta's AI progress is under dual pressure. On one hand, the competition with Chinese tech giants is intensifying. Companies like Alibaba, Tencent, and Baidu have accelerated their technological advancements since the release of DeepSeek earlier this year. Baidu, for instance, has made its ERNIE Bot service completely free, while Tencent has deeply integrated AI functionalities into its WeChat ecosystem. On the other hand, Meta must balance technological breakthroughs with cost control, adopting the MoE framework inspired by DeepSeek to reduce operational costs.

This delay highlights a broader challenge faced by large tech companies in the AI arms race. Despite the high-profile launch of the LLaMA 4 series in April, the commercialization of the core model still faces significant engineering hurdles. The development team is currently focused on improving the model's stability and output consistency, which did not meet expected standards in initial tests. This situation underscores the critical phase in which generative AI is transitioning from experimental stages to industrial applications, where technological maturity and engineering capabilities will determine the future landscape of global AI competition.

Industry observers note that Meta's ability to maintain its technological edge through architectural innovations will be a crucial factor in its future valuation. The company's success in navigating these challenges will not only impact its market position but also set a precedent for how other tech giants approach AI development and deployment.