en.Wedoany.com Reported - After investing $14.3 billion last year to bring in Scale AI founder Alexander Wang and his core research team, Meta comprehensively restructured its AI organization and launched its first closed-source AI model, Muse Spark, in April this year. The company has applied Muse Spark to businesses such as Facebook, Instagram, Meta AI services, and smart glasses to expand its AI footprint.
Meta's Superintelligence Labs (MSL), led by Alexander Wang, released Muse Spark, marking the company's first proprietary closed-source foundational model since moving away from its original open-source-centric strategy. This strategic shift was prompted by the Llama series released last year failing to meet expectations. Over the following year, Meta realigned its AI strategy, restructuring the organization, recruiting talent, and expanding investment in infrastructure.
However, the expansion of AI business still faces challenges. Due to repeated delays in releasing the API that allows external developers to use Muse Spark technology, Meta's developer ecosystem expansion has been hindered. The API, a key means of connecting AI models with external services, was originally scheduled to launch alongside the model in April but has yet to be officially released. Given that companies like OpenAI and Anthropic generate revenue through API sales, this strategy will play a crucial role in Meta's future AI monetization. A Meta spokesperson stated that the company has tested the Muse Spark API with some early partners and plans to make it publicly available this month.
Meta plans to expand AI investment this year, proposing capital expenditures of up to $145 billion, with the majority allocated to AI data centers and computing infrastructure construction. The company is also advancing the expansion of personal and enterprise AI agent services, as well as AI subscription models.
Recent changes have also occurred in organizational operations. In a recent letter to employees, CEO Mark Zuckerberg acknowledged trial and error during the AI transformation. It is reported that the company reassigned 7,000 employees to the AI organization last year but is currently adjusting some aspects of workforce management. Managing the operational costs of AI infrastructure has become a new challenge. Due to the cost burden from increased internal AI usage, Meta is considering setting usage caps per employee and expanding the development of in-house coding AI to replace external AI tools.
External variables have also emerged. Meta's acquisition of Chinese AI startup Butterfly Effect, aimed at securing next-generation AI agent technology, has recently initiated a withdrawal process. This follows a mandatory divestment order from the Chinese government citing national security concerns.
Investor focus remains on AI business outcomes. Meta's stock price has fallen 18% over the past year, the worst performance among major large-cap tech stocks. Although first-quarter sales this year grew 33% year-over-year, the highest growth rate since 2021, the market is still focused on the monetization potential of AI business and new revenue generation. In a recent interview, CEO Zuckerberg indicated that the company is also considering a cloud computing business utilizing excess AI infrastructure capacity, suggesting that the infrastructure supporting AI itself can be leveraged as a monetizable asset.
This article is compiled by Wedoany. All AI citations must indicate the source as "Wedoany". If there is any infringement or other issues, please notify us promptly, and we will modify or delete it accordingly. Email: news@wedoany.com
